{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style ='darkgrid', font_scale=1.5)  #设置背景\n",
    "plt.rcParams['font.sans-serif'] =['SimHei'] #设置字体，支持中文\n",
    "plt.rcParams['axes.unicode_minus'] =False\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T02:50:38.458839400Z",
     "start_time": "2024-04-15T02:50:38.443849200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 1、数据查看"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import os"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "data_user = pd.read_csv('tianchi_mobile_recommend_train_user.csv',dtype = str) #nrows=1000000，如果内存不够可以加上这个配置"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T02:51:42.065742900Z",
     "start_time": "2024-04-15T02:51:25.377964500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12256906 entries, 0 to 12256905\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Dtype \n",
      "---  ------         ----- \n",
      " 0   user_id        object\n",
      " 1   item_id        object\n",
      " 2   behavior_type  object\n",
      " 3   user_geohash   object\n",
      " 4   item_category  object\n",
      " 5   time           object\n",
      "dtypes: object(6)\n",
      "memory usage: 561.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#对于电脑比较弱（4G内存，cpu比较弱）就在这里选前100万条数据即可\n",
    "data_user.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T02:54:47.615854300Z",
     "start_time": "2024-04-15T02:54:47.584873200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "    user_id    item_id behavior_type user_geohash item_category           time\n0  98047837  232431562             1          NaN          4245  2014-12-06 02\n1  97726136  383583590             1          NaN          5894  2014-12-09 20\n2  98607707   64749712             1          NaN          2883  2014-12-18 11\n3  98662432  320593836             1      96nn52n          6562  2014-12-06 10\n4  98145908  290208520             1          NaN         13926  2014-12-16 21",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>98047837</td>\n      <td>232431562</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>4245</td>\n      <td>2014-12-06 02</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>97726136</td>\n      <td>383583590</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>5894</td>\n      <td>2014-12-09 20</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>98607707</td>\n      <td>64749712</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>2883</td>\n      <td>2014-12-18 11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>98662432</td>\n      <td>320593836</td>\n      <td>1</td>\n      <td>96nn52n</td>\n      <td>6562</td>\n      <td>2014-12-06 10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>98145908</td>\n      <td>290208520</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>13926</td>\n      <td>2014-12-16 21</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T02:55:02.412402800Z",
     "start_time": "2024-04-15T02:55:02.361431800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 2 数据预处理"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id          0.00000\nitem_id          0.00000\nbehavior_type    0.00000\nuser_geohash     0.68001\nitem_category    0.00000\ntime             0.00000\ndtype: float64"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.1 缺失值分析，apply默认对列进行操作，x代表一列\n",
    "data_user.apply(lambda x: sum(x.isnull())/len(x))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T02:56:18.895738300Z",
     "start_time": "2024-04-15T02:56:09.262516300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id                0\nitem_id                0\nbehavior_type          0\nuser_geohash     8334824\nitem_category          0\ntime                   0\ndtype: int64"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计多少条是空的\n",
    "data_user.apply(lambda x: sum(x.isnull()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T02:56:53.028827400Z",
     "start_time": "2024-04-15T02:56:43.555212700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "(12256906, 6)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T02:55:12.160800900Z",
     "start_time": "2024-04-15T02:55:12.148806900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 先对日期进行处理"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "#把日期和小时分别取出来，从日期，小时两个角度分析购物行为\n",
    "data_user['date'] = data_user['time'].str[0:10]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T02:59:04.927230Z",
     "start_time": "2024-04-15T02:59:02.510609200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "#把小时取出来\n",
    "data_user['hour'] = data_user['time'].str[11:]  #如果发生溢出就分开执行"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T03:00:15.392325Z",
     "start_time": "2024-04-15T03:00:12.060997700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id          object\nitem_id          object\nbehavior_type    object\nuser_geohash     object\nitem_category    object\ntime             object\ndate             object\nhour             object\ndtype: object"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:00:18.737227Z",
     "start_time": "2024-04-15T03:00:18.674265500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "    user_id    item_id behavior_type user_geohash item_category  \\\n0  98047837  232431562             1          NaN          4245   \n1  97726136  383583590             1          NaN          5894   \n2  98607707   64749712             1          NaN          2883   \n3  98662432  320593836             1      96nn52n          6562   \n4  98145908  290208520             1          NaN         13926   \n\n            time        date hour  \n0  2014-12-06 02  2014-12-06   02  \n1  2014-12-09 20  2014-12-09   20  \n2  2014-12-18 11  2014-12-18   11  \n3  2014-12-06 10  2014-12-06   10  \n4  2014-12-16 21  2014-12-16   21  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>98047837</td>\n      <td>232431562</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>4245</td>\n      <td>2014-12-06 02</td>\n      <td>2014-12-06</td>\n      <td>02</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>97726136</td>\n      <td>383583590</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>5894</td>\n      <td>2014-12-09 20</td>\n      <td>2014-12-09</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>98607707</td>\n      <td>64749712</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>2883</td>\n      <td>2014-12-18 11</td>\n      <td>2014-12-18</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>98662432</td>\n      <td>320593836</td>\n      <td>1</td>\n      <td>96nn52n</td>\n      <td>6562</td>\n      <td>2014-12-06 10</td>\n      <td>2014-12-06</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>98145908</td>\n      <td>290208520</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>13926</td>\n      <td>2014-12-16 21</td>\n      <td>2014-12-16</td>\n      <td>21</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:00:32.911679700Z",
     "start_time": "2024-04-15T03:00:32.892691600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "#类型转换\n",
    "data_user['date'] = pd.to_datetime(data_user['date'])\n",
    "data_user['time']= pd.to_datetime(data_user['time'])\n",
    "data_user['hour'] = data_user['hour'].astype(np.int8) #可以改为np.int8"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:01:42.127421600Z",
     "start_time": "2024-04-15T03:01:36.377383500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id                  object\nitem_id                  object\nbehavior_type            object\nuser_geohash             object\nitem_category            object\ntime             datetime64[ns]\ndate             datetime64[ns]\nhour                       int8\ndtype: object"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:01:44.737386Z",
     "start_time": "2024-04-15T03:01:44.720385400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "    user_id    item_id behavior_type user_geohash item_category  \\\n0  98047837  232431562             1          NaN          4245   \n1  97726136  383583590             1          NaN          5894   \n2  98607707   64749712             1          NaN          2883   \n3  98662432  320593836             1      96nn52n          6562   \n4  98145908  290208520             1          NaN         13926   \n\n                 time       date  hour  \n0 2014-12-06 02:00:00 2014-12-06     2  \n1 2014-12-09 20:00:00 2014-12-09    20  \n2 2014-12-18 11:00:00 2014-12-18    11  \n3 2014-12-06 10:00:00 2014-12-06    10  \n4 2014-12-16 21:00:00 2014-12-16    21  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>98047837</td>\n      <td>232431562</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>4245</td>\n      <td>2014-12-06 02:00:00</td>\n      <td>2014-12-06</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>97726136</td>\n      <td>383583590</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>5894</td>\n      <td>2014-12-09 20:00:00</td>\n      <td>2014-12-09</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>98607707</td>\n      <td>64749712</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>2883</td>\n      <td>2014-12-18 11:00:00</td>\n      <td>2014-12-18</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>98662432</td>\n      <td>320593836</td>\n      <td>1</td>\n      <td>96nn52n</td>\n      <td>6562</td>\n      <td>2014-12-06 10:00:00</td>\n      <td>2014-12-06</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>98145908</td>\n      <td>290208520</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>13926</td>\n      <td>2014-12-16 21:00:00</td>\n      <td>2014-12-16</td>\n      <td>21</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:01:49.620784Z",
     "start_time": "2024-04-15T03:01:49.531823300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "#对数据按时间进行一下排序\n",
    "data_user.sort_values(by ='time',ascending=True,inplace = True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:03:39.466250500Z",
     "start_time": "2024-04-15T03:03:27.816741300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id    item_id behavior_type user_geohash item_category  \\\n1505077    73462715  378485233             1          NaN          9130   \n8686537    36090137  236748115             1          NaN         10523   \n4035788    40459733  155218177             1          NaN          8561   \n10113411     814199  149808524             1          NaN          9053   \n2936757   113309982    5730861             1          NaN          3783   \n\n               time       date  hour  \n1505077  2014-11-18 2014-11-18     0  \n8686537  2014-11-18 2014-11-18     0  \n4035788  2014-11-18 2014-11-18     0  \n10113411 2014-11-18 2014-11-18     0  \n2936757  2014-11-18 2014-11-18     0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1505077</th>\n      <td>73462715</td>\n      <td>378485233</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9130</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8686537</th>\n      <td>36090137</td>\n      <td>236748115</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>10523</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4035788</th>\n      <td>40459733</td>\n      <td>155218177</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>8561</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10113411</th>\n      <td>814199</td>\n      <td>149808524</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9053</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2936757</th>\n      <td>113309982</td>\n      <td>5730861</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>3783</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:03:43.504539900Z",
     "start_time": "2024-04-15T03:03:43.467560200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id    item_id behavior_type user_geohash item_category  \\\n5241608   132653097  119946062             2          NaN          6054   \n10296029  130082553  296196819             1          NaN         11532   \n8527264    43592945  350594832             1      9rhhgph          9541   \n6263497    12833799  186993938             1      954g37v          3798   \n9200479    77522552   69292191             1          NaN           889   \n\n                        time       date  hour  \n5241608  2014-12-18 23:00:00 2014-12-18    23  \n10296029 2014-12-18 23:00:00 2014-12-18    23  \n8527264  2014-12-18 23:00:00 2014-12-18    23  \n6263497  2014-12-18 23:00:00 2014-12-18    23  \n9200479  2014-12-18 23:00:00 2014-12-18    23  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5241608</th>\n      <td>132653097</td>\n      <td>119946062</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>6054</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>10296029</th>\n      <td>130082553</td>\n      <td>296196819</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>11532</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>8527264</th>\n      <td>43592945</td>\n      <td>350594832</td>\n      <td>1</td>\n      <td>9rhhgph</td>\n      <td>9541</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>6263497</th>\n      <td>12833799</td>\n      <td>186993938</td>\n      <td>1</td>\n      <td>954g37v</td>\n      <td>3798</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>9200479</th>\n      <td>77522552</td>\n      <td>69292191</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>889</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:04:20.716505100Z",
     "start_time": "2024-04-15T03:04:20.678527200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "#丢弃原有索引，按位置重新生成索引，在原有df生效\n",
    "data_user.reset_index(drop =True,inplace =True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:06:29.288027100Z",
     "start_time": "2024-04-15T03:06:29.250049300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "     user_id    item_id behavior_type user_geohash item_category       time  \\\n0   73462715  378485233             1          NaN          9130 2014-11-18   \n1   36090137  236748115             1          NaN         10523 2014-11-18   \n2   40459733  155218177             1          NaN          8561 2014-11-18   \n3     814199  149808524             1          NaN          9053 2014-11-18   \n4  113309982    5730861             1          NaN          3783 2014-11-18   \n\n        date  hour  \n0 2014-11-18     0  \n1 2014-11-18     0  \n2 2014-11-18     0  \n3 2014-11-18     0  \n4 2014-11-18     0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>73462715</td>\n      <td>378485233</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9130</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>36090137</td>\n      <td>236748115</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>10523</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>40459733</td>\n      <td>155218177</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>8561</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>814199</td>\n      <td>149808524</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9053</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>113309982</td>\n      <td>5730861</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>3783</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:06:31.666862500Z",
     "start_time": "2024-04-15T03:06:31.624891500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "         user_id    item_id behavior_type user_geohash item_category\ncount   12256906   12256906      12256906      3922082      12256906\nunique     10000    2876947             4       575458          8916\ntop     36233277  112921337             1      94ek6ke          1863\nfreq       31030       1445      11550581         1052        393247",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>12256906</td>\n      <td>12256906</td>\n      <td>12256906</td>\n      <td>3922082</td>\n      <td>12256906</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>10000</td>\n      <td>2876947</td>\n      <td>4</td>\n      <td>575458</td>\n      <td>8916</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>36233277</td>\n      <td>112921337</td>\n      <td>1</td>\n      <td>94ek6ke</td>\n      <td>1863</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>31030</td>\n      <td>1445</td>\n      <td>11550581</td>\n      <td>1052</td>\n      <td>393247</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top是出现最多次数的那个str值，freq是top内的值出现的次数\n",
    "data_user.describe(include = ['object'])  #第二行可以看出行为类型，用户位置类别，商品品类"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:08:01.455479600Z",
     "start_time": "2024-04-15T03:07:35.941260600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 通过上面数据得出最火的商品是112921337，最火品类1863"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "                                time                           date  \\\ncount                       12256906                       12256906   \nmean   2014-12-04 04:47:28.445702144  2014-12-03 13:58:23.675396352   \nmin              2014-11-18 00:00:00            2014-11-18 00:00:00   \n25%              2014-11-26 15:00:00            2014-11-26 00:00:00   \n50%              2014-12-04 14:00:00            2014-12-04 00:00:00   \n75%              2014-12-11 23:00:00            2014-12-11 00:00:00   \nmax              2014-12-18 23:00:00            2014-12-18 00:00:00   \nstd                              NaN                            NaN   \n\n               hour  \ncount  1.225691e+07  \nmean   1.481799e+01  \nmin    0.000000e+00  \n25%    1.000000e+01  \n50%    1.600000e+01  \n75%    2.000000e+01  \nmax    2.300000e+01  \nstd    6.474778e+00  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>12256906</td>\n      <td>12256906</td>\n      <td>1.225691e+07</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2014-12-04 04:47:28.445702144</td>\n      <td>2014-12-03 13:58:23.675396352</td>\n      <td>1.481799e+01</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>2014-11-18 00:00:00</td>\n      <td>2014-11-18 00:00:00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2014-11-26 15:00:00</td>\n      <td>2014-11-26 00:00:00</td>\n      <td>1.000000e+01</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2014-12-04 14:00:00</td>\n      <td>2014-12-04 00:00:00</td>\n      <td>1.600000e+01</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2014-12-11 23:00:00</td>\n      <td>2014-12-11 00:00:00</td>\n      <td>2.000000e+01</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18 00:00:00</td>\n      <td>2.300000e+01</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.474778e+00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T03:08:32.117541700Z",
     "start_time": "2024-04-15T03:08:29.599978500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#可能是刷单的用户\n",
    "data_user[data_user['user_id']=='36233277']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#把时间作为类别\n",
    "data_user.describe(include = 'all')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 1 构建模型--计算pv和uv"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "#计算pv，后面选任何一列即可，可以选item_id，每一列是等价的\n",
    "pv_daily = data_user.groupby('date').count()['user_id']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:16:04.251052Z",
     "start_time": "2024-04-15T03:15:58.112292Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    366701\n2014-11-19    358823\n2014-11-20    353429\n2014-11-21    333104\n2014-11-22    361355\n2014-11-23    382702\n2014-11-24    378342\n2014-11-25    370239\n2014-11-26    360896\n2014-11-27    371384\n2014-11-28    340638\n2014-11-29    364697\n2014-11-30    401620\n2014-12-01    394611\n2014-12-02    405216\n2014-12-03    411606\n2014-12-04    399952\n2014-12-05    361878\n2014-12-06    389610\n2014-12-07    399751\n2014-12-08    386667\n2014-12-09    398025\n2014-12-10    421910\n2014-12-11    488508\n2014-12-12    691712\n2014-12-13    407160\n2014-12-14    402541\n2014-12-15    398356\n2014-12-16    395085\n2014-12-17    384791\n2014-12-18    375597\nName: user_id, dtype: int64"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#得到了每天的pv\n",
    "pv_daily"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:18:30.738172700Z",
     "start_time": "2024-04-15T03:18:30.695194700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "#如果不小心把名字弄错了，不需要重新读取数据，直接改列名\n",
    "#改为user_id1的目的是为了下面命名为pv,uv做准备\n",
    "pv_daily = pv_daily.rename('user_id1') #注意这里一定要写赋值"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:21:01.793715Z",
     "start_time": "2024-04-15T03:21:01.774724300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "#不设置下面内容，避免显示崩溃\n",
    "#显示所有列\n",
    "pd.set_option('display.max_columns', 30)\n",
    "#显示所有行\n",
    "pd.set_option('display.max_rows', 100)\n",
    "#设置value的显示长度为100，默认为50，最新pycharm可以拖动，因此无需设置\n",
    "# pd.set_option('max_colwidth',100)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:23:37.660496700Z",
     "start_time": "2024-04-15T03:23:37.648504400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "0            73462715\n1            36090137\n2            40459733\n3              814199\n4           113309982\n              ...    \n11881312     33984927\n11881313    125912657\n11881314     93755017\n11881315     48059114\n11881316     57808521\nName: user_id, Length: 248, dtype: object"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#是每一组的前3个,这里没用，没聚合\n",
    "data_user.groupby('date')['user_id'].head(8)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:26:42.124813200Z",
     "start_time": "2024-04-15T03:26:41.136376Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "     user_id    item_id behavior_type user_geohash item_category       time  \\\n0   73462715  378485233             1          NaN          9130 2014-11-18   \n1   36090137  236748115             1          NaN         10523 2014-11-18   \n2   40459733  155218177             1          NaN          8561 2014-11-18   \n3     814199  149808524             1          NaN          9053 2014-11-18   \n4  113309982    5730861             1          NaN          3783 2014-11-18   \n\n        date  hour  \n0 2014-11-18     0  \n1 2014-11-18     0  \n2 2014-11-18     0  \n3 2014-11-18     0  \n4 2014-11-18     0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>73462715</td>\n      <td>378485233</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9130</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>36090137</td>\n      <td>236748115</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>10523</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>40459733</td>\n      <td>155218177</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>8561</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>814199</td>\n      <td>149808524</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9053</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>113309982</td>\n      <td>5730861</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>3783</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T03:24:35.761955500Z",
     "start_time": "2024-04-15T03:24:35.719977900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "#如何计算uv，针对用户访问进行去重，再计算数目\n",
    "uv_daily = data_user.groupby('date')['user_id'].apply(lambda x:x.drop_duplicates().count())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:27:56.557393200Z",
     "start_time": "2024-04-15T03:27:54.241626500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    6343\n2014-11-19    6420\n2014-11-20    6333\n2014-11-21    6276\n2014-11-22    6187\nName: user_id, dtype: int64"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uv_daily.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:28:20.077046300Z",
     "start_time": "2024-04-15T03:28:20.043068500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "10000"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#得到所有的用户数量\n",
    "data_user['user_id'].nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T03:30:13.432497200Z",
     "start_time": "2024-04-15T03:30:11.311633200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id\ndate               \n2014-11-18     6343\n2014-11-19     6420\n2014-11-20     6333\n2014-11-21     6276\n2014-11-22     6187",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-11-18</th>\n      <td>6343</td>\n    </tr>\n    <tr>\n      <th>2014-11-19</th>\n      <td>6420</td>\n    </tr>\n    <tr>\n      <th>2014-11-20</th>\n      <td>6333</td>\n    </tr>\n    <tr>\n      <th>2014-11-21</th>\n      <td>6276</td>\n    </tr>\n    <tr>\n      <th>2014-11-22</th>\n      <td>6187</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#另外一种计算uv的方法\n",
    "uv_daily1 = data_user.groupby('date').agg({'user_id':'nunique'})\n",
    "uv_daily1.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:28:30.064589900Z",
     "start_time": "2024-04-15T03:28:27.672828200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [],
   "source": [
    "#pv和uv结合在一起，变为df\n",
    "pv_uv_daily = pd.concat([pv_daily, uv_daily],axis=1)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:31:19.412993700Z",
     "start_time": "2024-04-15T03:31:19.375995600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id1  user_id\ndate                         \n2014-11-18    366701     6343\n2014-11-19    358823     6420\n2014-11-20    353429     6333\n2014-11-21    333104     6276\n2014-11-22    361355     6187",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id1</th>\n      <th>user_id</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-11-18</th>\n      <td>366701</td>\n      <td>6343</td>\n    </tr>\n    <tr>\n      <th>2014-11-19</th>\n      <td>358823</td>\n      <td>6420</td>\n    </tr>\n    <tr>\n      <th>2014-11-20</th>\n      <td>353429</td>\n      <td>6333</td>\n    </tr>\n    <tr>\n      <th>2014-11-21</th>\n      <td>333104</td>\n      <td>6276</td>\n    </tr>\n    <tr>\n      <th>2014-11-22</th>\n      <td>361355</td>\n      <td>6187</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_uv_daily.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:31:21.384746900Z",
     "start_time": "2024-04-15T03:31:21.337777100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "#改列名\n",
    "pv_uv_daily.rename(columns={'user_id1':'pv','user_id':'uv'},inplace =True)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:31:25.911288500Z",
     "start_time": "2024-04-15T03:31:25.897285Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "                pv    uv\ndate                    \n2014-11-18  366701  6343\n2014-11-19  358823  6420\n2014-11-20  353429  6333\n2014-11-21  333104  6276\n2014-11-22  361355  6187",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pv</th>\n      <th>uv</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-11-18</th>\n      <td>366701</td>\n      <td>6343</td>\n    </tr>\n    <tr>\n      <th>2014-11-19</th>\n      <td>358823</td>\n      <td>6420</td>\n    </tr>\n    <tr>\n      <th>2014-11-20</th>\n      <td>353429</td>\n      <td>6333</td>\n    </tr>\n    <tr>\n      <th>2014-11-21</th>\n      <td>333104</td>\n      <td>6276</td>\n    </tr>\n    <tr>\n      <th>2014-11-22</th>\n      <td>361355</td>\n      <td>6187</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_uv_daily.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:31:27.745040600Z",
     "start_time": "2024-04-15T03:31:27.704063500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "#写入文件\n",
    "pv_uv_daily.to_csv('pv_uv.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:32:32.528702100Z",
     "start_time": "2024-04-15T03:32:32.479939900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "pv_uv_daily.to_excel('pv_uv.xlsx')  #大家用这个，安装pip install openpyxl\n",
    "# pv_uv_daily.to_excel('pv_uv.xls')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:33:33.734118500Z",
     "start_time": "2024-04-15T03:33:32.990450300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# pv_uv_daily.corr(method = 'spearman')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# pv_uv_daily.corr(method = 'pearson')  #皮尔逊相关系数,到推荐那里讲"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#上面说明每天的访问量和访问用户是正相关的关系"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    366701\n2014-11-19    358823\n2014-11-20    353429\n2014-11-21    333104\n2014-11-22    361355\nName: user_id1, dtype: int64"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_daily.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:35:43.167067800Z",
     "start_time": "2024-04-15T03:35:43.138081200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1944x648 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#默认显示一个月的区别\n",
    "plt.figure(figsize=(27,9))\n",
    "plt.subplot(211)  #等价于2,1,2，是2行1列图，这里是第一个图\n",
    "plt.plot(pv_daily, color='red')  #pv_daily是一个series，\n",
    "plt.title('每天访问量')\n",
    "plt.subplot(212)\n",
    "plt.plot(uv_daily,color='green')\n",
    "plt.title('每天访问用户数')\n",
    "plt.suptitle('UV和PV变化趋势')\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:36:35.076701700Z",
     "start_time": "2024-04-15T03:36:33.795120200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#结论：上面两幅图可以发现双12达到峰值"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 多维拆解，从小时维度观察pv和uv"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "#小时的pv,30天的某个小时\n",
    "pv_daily = data_user.groupby('hour').count()['user_id']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:39:06.208442700Z",
     "start_time": "2024-04-15T03:38:58.993766100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "hour\n0      517404\n1      267682\n2      147090\n3       98516\n4       80487\n5       88296\n6      158798\n7      287337\n8      396106\n9      485951\n10     550665\n11     526940\n12     531957\n13     598343\n14     594215\n15     598849\n16     576207\n17     505936\n18     547383\n19     735192\n20     935161\n21    1090178\n22    1088961\n23     849252\nName: user_id, dtype: int64"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_daily.head(30)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:39:15.634730400Z",
     "start_time": "2024-04-15T03:39:15.596755700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "#小时的uv\n",
    "uv_daily = data_user.groupby('hour')['user_id'].apply(lambda x:x.drop_duplicates().count())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:40:23.160694200Z",
     "start_time": "2024-04-15T03:40:20.888273800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "hour\n0     5786\n1     3780\n2     2532\n3     1937\n4     1765\n5     2030\n6     3564\n7     5722\n8     7108\n9     7734\n10    8139\n11    8239\n12    8314\n13    8352\n14    8255\n15    8257\n16    8320\n17    8228\n18    8278\n19    8538\n20    8780\n21    8866\n22    8599\n23    7484\nName: user_id, dtype: int64"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uv_daily.head(30)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:40:24.725714600Z",
     "start_time": "2024-04-15T03:40:24.702728800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "pv_uv_daily = pd.concat([pv_daily, uv_daily],axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:40:27.352495Z",
     "start_time": "2024-04-15T03:40:27.336503400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [],
   "source": [
    "pv_uv_daily.columns=['pv','uv'] #另外一种换索引名字的方式"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:40:29.263228500Z",
     "start_time": "2024-04-15T03:40:29.226252400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "data": {
      "text/plain": "          pv    uv\nhour              \n0     517404  5786\n1     267682  3780\n2     147090  2532\n3      98516  1937\n4      80487  1765",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pv</th>\n      <th>uv</th>\n    </tr>\n    <tr>\n      <th>hour</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>517404</td>\n      <td>5786</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>267682</td>\n      <td>3780</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>147090</td>\n      <td>2532</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>98516</td>\n      <td>1937</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>80487</td>\n      <td>1765</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_uv_daily.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:41:01.470265500Z",
     "start_time": "2024-04-15T03:41:01.440273700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "pv_daily.max()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "pv_uv_daily.corr(method = 'spearman') "
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "pv_uv_daily.corr(method = 'pearson')  #皮尔逊相关系数，看pv和uv相关性"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1944x648 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(27,9))\n",
    "pv_uv_daily['pv'].plot(color= 'steelblue', label ='每个小时pv')\n",
    "plt.legend(loc ='upper center')\n",
    "plt.ylabel('访问量')\n",
    "pv_uv_daily['uv'].plot(color= 'red', label='每个小时的uv',secondary_y =True)\n",
    "plt.ylabel('访问用户数')\n",
    "plt.xticks(range(0,24),pv_uv_daily.index)\n",
    "plt.legend(loc ='upper center')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T03:42:37.088582100Z",
     "start_time": "2024-04-15T03:42:36.080481300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 结论：每天的晚上9点，10点是购物高峰"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 多维拆解，从小时的维度，看不同的行为，点击、收藏、加入购物车和支付 "
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "data": {
      "text/plain": "     user_id    item_id behavior_type user_geohash item_category       time  \\\n0   73462715  378485233             1          NaN          9130 2014-11-18   \n1   36090137  236748115             1          NaN         10523 2014-11-18   \n2   40459733  155218177             1          NaN          8561 2014-11-18   \n3     814199  149808524             1          NaN          9053 2014-11-18   \n4  113309982    5730861             1          NaN          3783 2014-11-18   \n\n        date  hour  \n0 2014-11-18     0  \n1 2014-11-18     0  \n2 2014-11-18     0  \n3 2014-11-18     0  \n4 2014-11-18     0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>73462715</td>\n      <td>378485233</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9130</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>36090137</td>\n      <td>236748115</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>10523</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>40459733</td>\n      <td>155218177</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>8561</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>814199</td>\n      <td>149808524</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9053</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>113309982</td>\n      <td>5730861</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>3783</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:33:17.578487400Z",
     "start_time": "2024-04-15T06:33:17.565493300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [],
   "source": [
    "#先看下上面的数据\n",
    "pv_detail = pd.pivot_table(columns ='behavior_type',index ='hour' , data = data_user,values = 'item_id',aggfunc=np.size)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:34:02.832466300Z",
     "start_time": "2024-04-15T06:33:54.620319500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "(24, 4)"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_detail.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:34:11.139116200Z",
     "start_time": "2024-04-15T06:34:11.116130100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "behavior_type        1      2      3     4\nhour                                      \n0               487341  11062  14156  4845\n1               252991   6276   6712  1703\n2               139139   3311   3834   806\n3                93250   2282   2480   504\n4                75832   2010   2248   397\n5                83545   2062   2213   476\n6               150356   3651   3768  1023\n7               272470   5885   7044  1938\n8               374701   7849   9970  3586\n9               456781  10507  12956  5707\n10              515960  11185  16203  7317\n11              493679  10918  15257  7086\n12              500036   9940  15025  6956\n13              561513  11694  17419  7717\n14              558246  11695  17067  7207\n15              562238  12010  17289  7312\n16              541846  11127  16304  6930\n17              476369   9754  14515  5298\n18              517078  10342  14823  5140\n19              696035  13952  18853  6352\n20              885669  16599  25021  7872\n21             1030483  20397  30469  8829\n22             1027269  20343  32504  8845\n23              797754  17705  27434  6359",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>behavior_type</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n    </tr>\n    <tr>\n      <th>hour</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>487341</td>\n      <td>11062</td>\n      <td>14156</td>\n      <td>4845</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>252991</td>\n      <td>6276</td>\n      <td>6712</td>\n      <td>1703</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>139139</td>\n      <td>3311</td>\n      <td>3834</td>\n      <td>806</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>93250</td>\n      <td>2282</td>\n      <td>2480</td>\n      <td>504</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>75832</td>\n      <td>2010</td>\n      <td>2248</td>\n      <td>397</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>83545</td>\n      <td>2062</td>\n      <td>2213</td>\n      <td>476</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>150356</td>\n      <td>3651</td>\n      <td>3768</td>\n      <td>1023</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>272470</td>\n      <td>5885</td>\n      <td>7044</td>\n      <td>1938</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>374701</td>\n      <td>7849</td>\n      <td>9970</td>\n      <td>3586</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>456781</td>\n      <td>10507</td>\n      <td>12956</td>\n      <td>5707</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>515960</td>\n      <td>11185</td>\n      <td>16203</td>\n      <td>7317</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>493679</td>\n      <td>10918</td>\n      <td>15257</td>\n      <td>7086</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>500036</td>\n      <td>9940</td>\n      <td>15025</td>\n      <td>6956</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>561513</td>\n      <td>11694</td>\n      <td>17419</td>\n      <td>7717</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>558246</td>\n      <td>11695</td>\n      <td>17067</td>\n      <td>7207</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>562238</td>\n      <td>12010</td>\n      <td>17289</td>\n      <td>7312</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>541846</td>\n      <td>11127</td>\n      <td>16304</td>\n      <td>6930</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>476369</td>\n      <td>9754</td>\n      <td>14515</td>\n      <td>5298</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>517078</td>\n      <td>10342</td>\n      <td>14823</td>\n      <td>5140</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>696035</td>\n      <td>13952</td>\n      <td>18853</td>\n      <td>6352</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>885669</td>\n      <td>16599</td>\n      <td>25021</td>\n      <td>7872</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>1030483</td>\n      <td>20397</td>\n      <td>30469</td>\n      <td>8829</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>1027269</td>\n      <td>20343</td>\n      <td>32504</td>\n      <td>8845</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>797754</td>\n      <td>17705</td>\n      <td>27434</td>\n      <td>6359</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_detail #点击、收藏、加入购物车和支付 的比例"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:34:18.797729700Z",
     "start_time": "2024-04-15T06:34:18.741761600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id                  object\nitem_id                  object\nbehavior_type            object\nuser_geohash             object\nitem_category            object\ntime             datetime64[ns]\ndate             datetime64[ns]\nhour                       int8\ndtype: object"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:37:21.500029800Z",
     "start_time": "2024-04-15T06:37:21.425074400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "hour\n0      487341\n1      252991\n2      139139\n3       93250\n4       75832\n5       83545\n6      150356\n7      272470\n8      374701\n9      456781\n10     515960\n11     493679\n12     500036\n13     561513\n14     558246\n15     562238\n16     541846\n17     476369\n18     517078\n19     696035\n20     885669\n21    1030483\n22    1027269\n23     797754\nName: 1, dtype: int64"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_detail.loc[:,'1']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:39:33.696Z",
     "start_time": "2024-04-15T06:39:33.600971700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x648 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,9))\n",
    "pv_detail.loc[:,'1'].plot(color= 'steelblue', label ='点击')\n",
    "plt.xticks(range(0,24),pv_detail.index)\n",
    "plt.legend(loc ='upper center')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:44:22.119213100Z",
     "start_time": "2024-04-15T06:44:21.772412Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x648 with 1 Axes>",
      "image/png": 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wsrOzmTx5EnXr1qdmzdr/+fwWi4ULLqhGw4aNSU9Pw+WKYNasaQAsX/4BHo+H2247sbx66aU5NGjQEJ/Px+bNP3PXXb05diweAI/Hg8lkYv781ylVqjRLlizC6/XickVy+PBBKlWqzL59e/nwwzXY7XY2bFjHpEnjCQkJwWQyk52djdfrweVy0a9f7zzP6/F4yc52M3nydBo3vuQM32URETGUx0PY9CmEPfcMgVA7qZOnkXVbj8K9K9058lepSvqwETjHjsT20TKyO19tdKRiS6WTiIiISD5btGgBixcvyn08fPig3D936NCZqlWrUbFiZe699wEAli1biu80C5oOGvQQa9euYfr0F5kyZcZ/HvvXyKQ1a1by8cermTTpBcxmM6mpqaxYsZypU2dRt2793OPdbjedO7chJCRnGl18/FF8Ph+DBz+Qe0zr1pdz44238MUXn/L662/x2muzqVy5ClWqXMBLL73I0KGP8OCD9xMREQFAp05X06lTzof4jIx0eva8ja5du9GjR08mT55Imzbtadiw0WneSRERCUbWH7/HNXgA1l+24u56HWnjJ+I/j1G9Rsi87wHs772Dc8QwEpu3IOCKMDpSsaSFxEVERETyWf/+g/nss6+ZMWM2AKtWfca6dd/Tvn0nnE4nNlsIERGRNGlyGU2aXEbZsuUIOc26F7/9touMjAx++OFbtm3bckY5LBYrUVFRAJhMJubMmUX9+g0oXboM6elphIaGEhoaitVq+fP4nI+G27ZtoWLFSqxY8SlDhjxMxYqV+fDDNXz//be0atWWatVqsH37NqpVq8HRo78TGRlFRkY64eHhuVMK/+L3+xk37klq1KjJHXfcBcDSpYs5eHD/Gb+fIiISJNLSCH/iEaI6X4UpMYHkNxaQMmdukSucAAgJIfW5KZh/P0LYhLFGpym2VDqJiIiI5DObzYbVaiU9PQ2r1Up4eM5de9LS0nA6XZhOMvXgn+smnczbb8+nWbOWVKtWnXfeWfCfx/51eavVit1uz91+ww0307//YN5//z0eemhQ7nav1/dnhpzC6KuvvqR+/YtwuVzY7XbMZhPR0dG88MJL9O8/iN9+20V8/FEuuaQJR4/+TunSpcnIyDhhPabMzEzGjRvFoUMHePzx0bmvOyTElqec8nq9ZGRk/OdrEhERY9k+WU1My6aEzZpB1p29SFz3Ldmdupz+xCDmvaQJWb3uwTHnZaw/fm90nGJJ0+tEREQkqIQufAv7gvlGxyCr+x24b7ntvK6RmJhITExs7uOc9ZVcZ32dPXt+49NPP+HZZ19k7949vPTSiyQkHM9z7X8KBP76PYDT+ffzVapUGa/Xz08//cAtt9yO1+vF7/fj9/sBchcRv/LKFrnT5P4pLi4OgIUL3+KSSy4lKiqKQ4cOULVqNTIy0gkNDc1z/IIF81i9egUA7du3yLNv3LhRjBs3Ks+2FSs+Paf3R0RECo7pjz9wPvEI9vfewXthTRI/WIW36eVGx8o36Y+NwvbRMlxDHyRxzedgVU2Sn/RuioiIiBSQAwf2U6lS5dzHf410yshIP6vrTJv2ApUrV6FJk8uoU6cer7zyEm+/PZ9+/R486fF/lUiZmRm5pdNfa0YdPHiAY8eO0abNVQwceB9XX30tl12W883DX6OP2rZtl+d6mZmZzJ37Kunp6XTteh1LliziqacmEggE2L79F9q370RiYmKeUVUAV199LbGxcVSvfiHR0dHYbDZMJjO3334Dffs+QKtWbYCchcQ9nmzCw3XnOhGRoBEIEPrOApwjR2BKTSV96MNkDBoG//oBQ1EXcEWQNn4SkXffgWPWDDIfGGh0pGJFpZOIiIgEFfctt533CKNg8euvO6hR4+/bRv810ulsSqdPPlnDN9+s54UXZmAymXC5XFxzzXUsWfIu119/C2XLnriORiCQUzodPnyIcuXKA38XUXPmzOLGG2/BarVSv/5FfPnlZzRpchnwd+l06NBBtm/fxqZNP7F+/VccP/4H3377NZ07d2X06MepUaMml1/ejI0bfyAtLZXGjS9h+fIPTiidSpcuw7XXXn9CPpPJhNPpJDY27ozfBxERKTzmA/vhjiFErF6N53+Xkvr8VHy16xgdq8Bkd+mKu2NnwieNx931WvyVqxgdqdjQmk4iIiIiBcDj8bB5889cdFGj3G1/jXQ6U7//foTnnnuatm3b8b//XZq7/fbb7wJMjBz5CF6v94Tz/tr212LfISEhVK9+ITt2bGfNmpW8/fabtGlzBQsXvsl3331DZmbOekp/Ta97++03GTPmCY4cOUL9+g2oWLES06a9TFJSEnv27GbEiJEAfPDBEho1upiyZcuRkZGOzZbz0+9AIEBGRgYej+eMX2sgEMDj8eB2u8/4HBERKQBuN5E3XQvr15M64VmSlq0u1oUTACYTaeMnASacDw/5e566nLczHum0YcMGPvzwQ7KysmjatCk33nhj7oKXH330EVOmTCEhIYGOHTvy+OOP55nTv337dkaPHs327dtp2LAhEyZMoFy5crn7ExMTGTNmDF988QVly5Zl5MiRXHbZZbn7/X4/L774IosWLcJsNtOnTx/uvPPO/Hj9IiIiIgViw4av8Hg8XHJJTlmUU8Skn3HplJKSwsMPD8blcjFkyMN59sXExNKrVx9mzJjCY489xJgxEwgN/XuUUf/+g/n99yPMnfsq1apV548//uD119/CajVzzz33Ua9efapXvxC73cHtt9/IgQMHgL9HOt1xx110734H5ctX4KOPPmTHjl8AuO22Hlx5ZXOqVKnK9u2/8Mknq5k8eToAGRkZ2Gw2ABISjnPttR3/8/WNHTuSsWNHnrC9Vas2jBs38YzeIxERyX+Ol1/C+ttuWLGCrEuuNDpOofFXrET6iMdxPjGC0KWLcV93g9GRioUzKp2WL1/Oo48+yrXXXktMTAxPPfUU27dvZ+TIkaxfv56hQ4dy00030a5dO6ZPn8748eMZPXo0AMePH6dnz55Ur16dKVOmsGrVKvr27cuSJUtyf5o2YMAA9u3bx/jx4zly5Aj3338/S5cupVKlSgBMnTqV2bNnM2zYMCpUqMDIkSOJi4ujc+fOBfS2iIiIiJyf+fNfp2XLNjidOXeuS01Nxe/343Q6CQQCfPvtBpo1+1/u8e3a/V3SxMcfZfjwnOJo1qzXiYyMOuH6t956O1u3buLzzz+lf/++PPLISKpXrwHkLBj+2muv0KxZCz777BPefns+06fPplatmvTseU+e67z33jIOHsxbOpX5j1tfV6lSleTkJEaNGsFVV3XgkkuaAJCRkY7d7gAgKiqaBQsWExISgtUawr9v1te9+w3ce+/fazpBTinn9fpyPx+KiEjhMx/9nbDnJ+Lu0InQjh3hWKrRkQpV5j33EfruOzgfe5js1m0JnOTfXzk7p/1XPTs7m6eeeoqRI0dyww05TV/VqlWZOHEiI0aM4MUXX6RJkyaMGTMmd1/Hjh0ZMGAAcXFxvPHGG/h8PmbOnInL5aJZs2a0b9+eNWvW0KlTJ9atW8d3333H/PnzadIk50PLjh07ePXVVxk1ahRpaWnMmTOH++67j549ewI5Q9OnT5+u0klERESC0gcfLGHHjl8YMWIkWVlZvPRSzl3n7HY7cXGl8Hg8XHRRIx5++HEgZzpbamoKACtXLmfq1Ofxer08/fTzVK16wUmfw2w28+ST43nsseGsX/8lgwb14803FxEREcnq1Sv57LNPeOONtylXrjw//7yRRx4Zwuuvv0l4uOuE62zbtgWr1YrDEXbC8/j9vjyP9+3byyOPDMFutzN06MN/HuNn9+7dXHRRQyCnvPrnAur/FggECAsL05pOIiJBJnzsKEyebNJGj6d4LRd+hiwW0p6bQlT7VoSPfZK0Z18wOlGRd9o1nbKyshg4cCDXXXdd7rYyZcqQnZ2N2+3m559/5uqrr87dV6lSJapXr87XX38N5EzLa9GiRe7tb81mM61atWLDhg25+8uUKZNbOAG0adOG9evXA/DDDz/gdrvzPEfr1q3ZtWsXR48ePY+XLiIiIpL/UlKSmTNnFtdffzMXXFANu93O7t27MJlMjBgxitDQULxeL3a7gypVqlKlSlVKlSpFSEgIAE6ni5AQG9OmvULjxpf853OFhIQwYcKz3HLLbQwbNoKIiEi2b9/GU0+NYsCAIVSqVBmr1cro0eOxWKzs3bsnz/nffvs17du3ZNy4UbRufVXu9Lh/ysjIzF0j6quvvuTOO2/BbrczefJ0/vjjD26//Ua6devMgQP7aNmyzQnnn4zf78td2FxERIKD9ftvsb+zgMz7B+CvVt3oOIbxXtSIzL79cMx9Fes3Xxsdp8g77UiniIgIbr311tzHWVlZvPXWW7Rq1Yrjx4/j9/upWbNmnnMqVqzI3r17ATh69Cjt2rU7Yf/atWtz91944YUn7D906BA+n4/4+HhCQ0OpUuXv1eOjoqJwuVzs37+fMmXKnN0rFhERESlAERGRzJ37dp47uU2b9nKeY/49xe3uu/vm/rlZsxY0anRx7rS807FYLAwYMCT3ce3adZk8eToXX/z31L2IiEjeeGMB4eEOvN6/y56LL/4fd9/dh7p161O//kUnvX5WVmbu4t5XXtmcYcNG0L59R0JD7cTExNKkSVNiY2Np3rzVKUdl/ZvH48HjyT6jY0VEpBD4/TgffQhf2XKkPzjU6DSGSx/+KKHLluIaNpDET9bBSX4oI2fmrCbNT5s2jXfeeYfIyEjeeOMNjh07BpA7iukvYWFhJCYmAuB2u0/YHx4enmd/RETECed7PB5SUlLIysrC5XJh+tdiAGFhYSQkJJxNfBEREZFCcbI1mM7GmRZOp/LPwukv/7zJy1+sViu33nrHf17rzjvv5s4778593LXrdXn2Dxo07KzzffHFt2d9joiIFBz7228S8tNGUma8Auf5b1Cx4HSS9vSzRN5xC2EzXiTjHP6tkxxnVTo1aNCAPXv2sHr1alavXp17h7m/Fp38i8lkIisrC8gZ9v3v/UDu/pMN4/6rYHK73dhstty75J3qOc5UbGzx+ctTqtSZ3245GChvwVLegqW8Bauo5YWilzlY88bHm7FaT/w3/mTbgpnyFqx/5zWbzUH7NQ3B+/ftVJS34BW1zMpbsIIyb3IyjB8NV1xBxH29+efdH4Iy73/I17y33wzvLyL8uWcI79UDatTIv2v/qai9v+firEqnli1b0rJlS1577TXGjRvHV199BUB8fDxVq1bNPS4pKSl3OlxsbCzx8fF5rpOUlITDkXN3k5iYGLZv337CfgCHw0FsbCwJCQn4fL485VVycjJhYScudvlfjh9Pw+8PnNU5wahUKRfHitBdBJS3YClvwVLeglXU8kLRyxzMef1+f56pXpBTMPx7WzBT3oJ1srx+vz9ov6aD+e/byShvwStqmZW3YAVr3vCRj+M4doykNxfh/SMtd3uw5j2VgshrHvkU0atW4727D8nvLuWE27Geh6L2/p6K2Wz6zwE+p/1Rl9fr5eDBg3m2tWrVCo/Hw4EDByhfvjwbN27M3RcIBNi2bRulS5cGoHbt2nn2A2zdujXP/m3btuWuFQCwZcsW7HY7ERER1KpVC6/Xy6ZNm3L37969m8zMzNxriIiIiIiIiMjZsezcgWP2TLLuuAtvw8ZGxwk6/rLlSH9sFLYvPyN00dtGxymSTls6bdq0iY4dO+Ypnvbu3YvZbKZcuXJ06NCBhQsXkpLy121+V/LHH39wxRVXANChQwc2bNjA5s2bATh8+DBr167N3d+6dWvcbjcLFiwAchaWXLhwIZdffjkmk4lKlSpRr149Zs+enfv88+fPJzIyknr16uXT2yAiIiIiIiJSggQCOB9/mEBYOOkjRhqdJmhl9eyN55ImOEc9iun4caPjFDmnnV7XuHFj6tatS9++fRkyJOfOKOPHj6dLly7ExsbSp08fli1bxo033kijRo1YsWIFbdq0oX79+kBOqdS0aVPuvvtu2rRpw/r164mJieHmm28GcqbX9evXj0mTJvHjjz9y4MABfv31V8aOHZubYfjw4fTu3ZsePXrgcDj4/PPPGTFiBFbrWc0OFBERERERERHAtmoFts/WkjbuaQJxcUbHCV5mM6nPvUj0Vc0JH/MEaVNmGJ2oSDntSCeTycSMGTOoU6cOjz76KE8++SSdOnXiqaeeAnLWbHrvvfdo1KgRO3fu5K677mLy5Ml5zp85cya33XYbO3fu5NJLL2XBggV57srSr18/xo0bR3x8PJGRkcybN4+LLvr7tr1NmzZl/vz52Gw2EhMTGTduHD179szHt0FERERERESkhMjKwvnEI3hr1SazVx+j0wQ9X916ZPYbiGPBfEK++tLoOEXKGQ0ViouL47nnnjvl/jJlyjBx4sRT7rfZbAwePJjBgwef8phu3brRrVu3U+5v3Lgxc+bMOZO4IiIiIiIiInIKjlnTsezbS9KipRASYnScIiF9yHBCly7GOexBEj9dD3a70ZGKhKJ1z1wRERGREiQ1tejf1UZERIKL+fAhwidPwt25K56WrY2OU3SEhZE6cTLW3bsIm3LqQTmSl0onERERkUKwa9evfPHFZ2d8/B9/HOOmm7ryzTcbTrp/3rzXmDv31XxKJyIiJUX4mJHg85E2+imjoxQ5ntZtybr+JsJefB7Lzh1GxykSVDqJiIiIFILvv/+GefNeO+PjP//8U3w+H7Vr1znp/l9+2cZOfeAVEZGzYP3ma+yLF5HxwED8VaoaHadIShv7NIHwcJzDHgS/3+g4QU+3fxMRERHJRxkZ6bjdbiwWKyaTKXe73x/AYrGcMGXO7/fh8XiJ+9edg1at+oiuXa8jMjLqpM8TEmLFZgvN9/wiIlJM+Xw4H30IX/kKZAwYYnSaIitQqhTpo8bhGtwf+4L5ZN1+p9GRgppKJxEREZF89N57i5g1a9op93fqdOL6GU6nk5UrP8t9vGnTT2zbtoWHH36cjIwMLBYLoaGnLpg8Hg8WiwWzWYPYRUTk5OxvziVk88+kvPwahIcbHadIy+p+B6EL3yJ89OO423ciUKqU0ZGClkonERERkXx00023cu211xMaasNi+fuj1gcfLGHt2jW88MKMPMf7fD7cbneebfPnvwFAaGgow4cP4qeffjzl861YsQyAt99eQsWKlfLrZYiISDFiSkokfMIYsi+/Eve11xsdp+gzm0l7dgrRra/A+cQjpM6cY3SioKXSSURERCQf2e127Ce5jbLZnDPVzmrN+/HLarXmGcX0/fff8s0363MfjxgxEp/PS0iILc95kydPwmYL4YEHBpGdnU3p0mXy82WIiEgxEjZpAqbERNLGPQP/mPot585XsxYZA4cQ/twzZN3cHU+bq4yOFJQ0BltERESkAGRlZeHz+c7qnIyMDCZNGk/Xrt1yt1WoUJHKlatiMpkpV6587i+Hw47DEUa5cuWpUqUqNpvtP64sIiIllWX7LzhefYWsHr3wNbjI6DjFSsaDQ/FWr4Fr+BDIyDA6TlDSSCcREREJKgu3v8WC7fONjkH32ndwS+3bzvn8Dh1anrR0atbsf7l/vuOOntx3X//cx++88xZms5kePXry/vvv5m7fv38fPXvexiuvvEH16jXOOZOIiJQwgQDOxx4m4HKR/sjjRqcpfux20p6dQlS3LoQ/P5H0x580OlHQ0UgnERERkQLw+eff8PLLr+NwhLF8+cesW/d97q8XX5yJ1WrlhhtuznPOzTffxvjxzxIamnd6XqVKlSlbtixvvvlGYb4EEREp4mzLP8T25WekP/w4gdhYo+MUS54rm5N16+04ZryIZdtWo+MEHY10EhERkaByS+3bzmuEUTCpW7c+devW4/XX5/Dgg0MBCAQCvPLKDLp1u4lSpUrnOT4sLIwLLqhGUlJSnu0mk4nrrruBl16aSv/+g4iJ0TcOIiJyGpmZOJ98DG+demTddbfRaYq1tCfHYVuzEtfQgSQtXwO6m2wuvRMiIiIiBah//0EsXbqYn3/+CYBFi97myJEj9OrV56yu06ZNe66++jqs1pACSCkiIsVN2IwXsezfR9pTz4BV400KUiAmlrTR4wn54Tvsr+tOdv+k0klERESkAF14YS169uzNY489xLx5r/Pyy9MZOXIsERERZ3WduLg4hg59+KzPExGRksd88ABhLz5P1jXd8DRrYXScEsF9061kN29F+FOjMf9+xOg4QUOlk4iIiEgBu/76m3E4wpg1axrNm7eiUaOLjY4kIiLFWPiYJyAQIH3UWKOjlBwmE2mTnseU7cb52MNGpwkaKp1ERERECsiePb8xa9Z0une/nqpVqzJ48EP88MN39OzZnXfeeYv9+/fi9/tPOC8QCOT5/WQ8Hi8mk6nAsouISNEUsn4d9vcXkzFgMP5KlY2OU6L4qtUgY8hwQj98H9vqFUbHCQqa2CkiIiKSj1auXM7atWvYvv0XkpOTuOKKZowZM4HGjS8BoFOnq1m6dAmLFy/ixRefx2YLJSYmhokTX6BateoA+P2+P38/sZBaunQx+/fv5bvvvua22+4svBcmIiLBz+vF+ehwfJUqk9F/kNFpSqSMBx4kdMm7OB8ZRsIVzcHpNDqSoVQ6iYiIiOSjK65oxqZNP3H//Vdx5ZXNiYiIzLM/LCyc7t3voHv3O9i3by9bt24mNTUlt3AC8Hq9OJ1OvF7vCddPSUlh7dqPueqqDtx66x0F/npERKTosM97Heu2LSTPmQsOh9FxSiabjdRJU4ju2p7wieNJHzPe6ESGUukkIiIiko8iIiIZPvyxMzq2SpWqVKlS9YTtZcqUZeXKz056To8ePenRo+e5BxQRkWLJlJhA+NNjyW7WguyrrzU6TonmvawpmT164Xh5Bu4bb8Z7USOjIxlGazqJiIiIiIiIFHHhzzyFKSWFtHHPgNb8M1z6E08SiI3DOfRBOMnI5ZJCpZOIiIiIiIhIEWbZugX763PI6tkbX916RscRIBAVTdpTzxDy80Ycc2YZHccwKp1ERETEMP91dzaRf9PXi4jISQQCOB8bTiAqivThjxqdRv7Bfe31uNu2I3zCOMwHDxgdxxAqnURERMQQFosVjyfb6BhShHg82VgsWpJUROSfbB++j239OtIfeYJAdIzRceSfTCbSnnkeAn6cI4ZBCfzhiUonERERMYTTGUVS0jGys90awSL/KRAIkJ3tJinpGE5nlNFxRESCR0YGzlGP4a3XgCzdZCIo+StXIf2hRwldtQLb8g+NjlPo9KMiERERMYTDEQ5AcvIf+Hw5C2yazWb8fr+Rsc6K8hasf+a1WKy4XNG5XzciIgJhUydjOXSQpJdmg8VidBw5hcx7+2F/7x2cjz5EYouWBCIijY5UaFQ6iYiIiGEcjvA8JUKpUi6OHUs1MNHZUd6CVdTyiogUJvP+fYRNn0JWtxvwNL3C6DjyX0JCSH1uClGd2hI+fgxpTz9ndKJCo+l1IiIiIiIiIkWM88nHwWwmfeRYo6PIGfBe/D8ye/fF/tpsrN9/a3ScQqPSSURERERERKQICfnyc0KXLSXjwaH4K1Q0Oo6coYwRT+AvWw7XsEHg8Rgdp1CodBIREREREREpKrxenI8Nx1e5Khn3DzA6jZyFgCuCtPGTsG7bApMnGx2nUKh0EhERERERESki7K/Pxrr9F9LGjAe73eg4cpayu3TF3elqePllo6MUCi0kLiIiIiIiIlIEmI4fJ/yZ8WS3bE12py5Gx5FzlPLK65TypRsdo1BopJOIiIiIiIhIERA+YSymtFTSxj0DJpPRceRc2WxQubLRKQqFSicRERERERGRIGfd/DP2ea+Rec+9+GrVNjqOyBlR6SQiIiIiIiISzAIBnI8OJxAbS8awR4xOI3LGtKaTiIiIiIiISBALXfIuId9sIPX5qQQio4yOI3LGNNJJREREREREJFilpxM++gk8DRuT1f0Oo9OInBWNdBIREREREREJUmEvPoflyGFSXnkDLBaj44icFY10EhEREREREQlC5r17CJsxlawbb8F76WVGxxE5ayqdRERERERERIKQc9RjYLGSPnKM0VFEzolKJxEREREREZEgE/LpJ4SuWEb6kIfwly1ndByRc6LSSURERERERCSYeDw4n3gEX9ULyLz3AaPTiJwzLSQuIiIiIiIiEkQcr76MdecOkucvhNBQo+OInDONdBIREREREREJEqZjxwibOIHsNleR3a6j0XFEzotKJxEREREREZEgET5+NKbMDNLGPQMmk9FxRM6LSicRERERERGRIGD96Ufsb80js28/fDUuNDqOyHlT6SQiIiIiIiJiNL8f56PDCcSVImPocKPTiOQLLSQuIiIiIiIiYrDQdxcS8v23pLz4EgFXhNFxRPKFRjqJiIiIiIiIGMiUlkr42FF4Lr4E983djY4jkm800klERERERETEQGGTn8Vy9HdSXn8TzBobIsWHvppFREREREREDGL5bReOWdPJuvV2vJc0MTqOSL5S6SQiIiIiIiJikPCRjxKwhZL22JNGRxHJdyqdRERERERERAxg+2Q1oatXkjH0YQJlyhgdRyTfqXQSERERERERKWzZ2YQ//gje6jXI7HOf0WlECoQWEhcREREREREpZI5XZmLdvYvkBe+CzWZ0HJECoZFOIiIiIiIiIoXIdPQoYc89g7t9R7Lbtjc6jkiBUekkIiIiIiIiUoicTz2JKdtN2pgJRkcRKVAqnUREREREREQKifWH77C//SaZ9z6Av1p1o+OIFKgzKp2OHz/OwIEDady4MRdddBH33Xcfx48fByA+Pp5atWqd8Gvfvn2552/fvp3u3bvTuHFjevbsyZEjR/JcPzExkcGDB3PJJZfQpUsXvvnmmzz7/X4/L7zwAldeeSXNmzdn7ty55/u6RURERERERAqX34/zseH4ypQlY/Awo9OIFLgzWkh80KBBHDx4kMGDB+Pz+Zg2bRojRozg5ZdfZtu2bcTExPDyyy/nOadcuXJATmHVs2dPqlevzpQpU1i1ahV9+/ZlyZIlWK05Tz9gwAD27dvH+PHjOXLkCPfffz9Lly6lUqVKAEydOpXZs2czbNgwKlSowMiRI4mLi6Nz5875+V6IiIiIiIiIFJjQdxYQ8uMPpEx/mYDTZXQckQJ32tJpw4YNbNmyhRUrVlC2bNnc7RMnTiQ9PZ1t27ZRq1YtGjRocNLz33jjDXw+HzNnzsTlctGsWTPat2/PmjVr6NSpE+vWreO7775j/vz5NGnSBIAdO3bw6quvMmrUKNLS0pgzZw733XcfPXv2BCAtLY3p06erdBIREREREZGiISUF59hReP53Ke4bbzE6jUihOO30uvr167No0aI8hVN0dDR+vx+/38+2bduoX7/+Kc/fsGEDLVq0wOXKaXHNZjOtWrViw4YNufvLlCmTWzgBtGnThvXr1wPwww8/4Ha7ufrqq3P3t27dml27dnH06NGzfLkiIiIiIiIiBhg7FtMfx0gbPxFMJqPTiBSK05ZOLpeLGjVq5Nn25ZdfUqNGDVwuF9u2beOLL76gadOmNG7cmPvuu4+9e/fmHnv06FFq1aqV5/yKFSvmHnP06FEuvPDCE/YfOnQIn89HfHw8oaGhVKlSJXd/VFQULpeL/fv3n+3rFRERERERESlUll93wgsvkHX7nXgbXWx0HJFCc9Z3r9u1axcrVqygZ8+eJCQkcOjQIcqXL8/TTz/NhAkT2LdvH3369MHn8wHgdrtzRzn9JTw8nMTExNz9ERERefaHhYXh8XhISUkhKysLl8uF6V9NcFhYGAkJCWcbX0RERERERKRQhY96FMLDSR8x0ugoIoXqjBYS/4vP52PEiBHUrFmTbt264ff7WbRoEfXr18dszumv6tSpQ4cOHVi3bh0tW7YkJCQEi8VywrWysrIAsNlsJ+z7q2Byu93YbLbca//7mL+ucaZiY51ndXwwK1WqaC06p7wFS3kLlvIWrKKWF4peZuUtWMpbsJS3YClvwStqmZW3AHz5JXy8GiZOJK5uNaPTnJUi8f7+g/IGn7MqnWbMmMGOHTtYtGhR7p3nLrroojzHVKlShdjYWLZv307Lli2JjY0lPj4+zzFJSUk4HA4AYmJi2L59+wn7ARwOB7GxsSQkJODz+fKUV8nJyYSFhZ1NfI4fT8PvD5zVOcGoVCkXx46lGh3jjClvwVLegqW8Bauo5YWil1l5C5byFizlLVjKW/CKWmblLQCBAJEPj8BSpiyWBx4I/rz/UCTe339QXmOYzab/HOBzxtPr1q1bx4wZMxgxYkTuGk3Hjx9n48aNeY7zeDykpaXhdrsBqF279gnHbN26ldKlS+fu37ZtW+7xAFu2bMFutxMREUGtWrXwer1s2rQpd//u3bvJzMzMvYaIiIiIiIhIsAn5/FNsG74iY9AwOMtBEyLFwRmVTjt37mTQoEF06dKF7t27525ft24dAwYMyDPNbeXKlWRlZdGwYUMAOnTowIYNG9i8eTMAhw8fZu3atVxxxRVAzp3o3G43CxYsAHJKq4ULF3L55ZdjMpmoVKkS9erVY/bs2bnPMX/+fCIjI6lXr955vnwRERERERGRAhAIEP70WHwVK5F1x11GpxExxGmn13k8HgYOHIjNZuOWW27JLY8A2rZty+TJk+nbty9du3bl8OHDzJkzh0aNGtG8eXMgp1Rq2rQpd999N23atGH9+vXExMRw8803AznT6/r168ekSZP48ccfOXDgAL/++itjx47NfZ7hw4fTu3dvevTogcPh4PPPP2fEiBG5U/xEREREREREgolt9UpCfvyB1MnTIDTU6Dgihjhta7Nz50727NkDwB133JFn39y5c3nllVcYM2YMY8eOJTo6ml69etGvX7/cxb9NJhMzZ85k+vTpfPHFF1x66aUMGzYMp/PvOX/9+vWjXLlyLFy4kMjISObNm5dnraimTZsyf/58pk2bRmJiIuPGjeOmm27KlzdAREREREREJF/5/YQ/PQ7vBdXIurn76Y8XKaZOWzrVq1ePHTt2/Ocx8+bN+8/9NpuNwYMHM3jw4FMe061bN7p163bK/Y0bN2bOnDn/HVZERERERETEYLZlS7Fu3UzKS7MhJMToOCKGOeOFxEVERERERETkNHw+wp95Cm/tOrivu8HoNCKG0qJIIiIiIiIiIvkk9N2FWH/dSfKr88FiMTqOiKE00klEREREREQkP3g8hE96Gk+DhmR36Wp0GhHDaaSTiIiIiIiISD6wL5iPZf9e0t5aBCaT0XFEDKeRTiIiIiIiIiLnKyuLsOcn4vnfpWS3bW90GpGgoJFOIiIiIiIiIufJMe81LIcPkTptlkY5ifxJI51EREREREREzkd6OmEvPEd285Z4mrUwOo1I0NBIJxEREREREZHz4JjzMuZj8aS/9qbRUUSCikY6iYiIiIiIiJwjU0oyYdMm476qPd5LLzM6jkhQUekkIiIiIiIico4cs2ZgTkoi4+HHjI4iEnRUOomIiIiIiIicA1PCcRwzp+Pucg3eho2NjiMSdFQ6iYiIiIiIiJyDsBlTMaWlkq5RTiInpdJJRERERERE5CyZ4uNxzJ6J+/qb8NWuY3QckaCk0klERERERETkLIVNfR7cbjIeesToKCJBS6WTiIiIiIiIyFkwHz6E4/U5ZN16O75qNYyOIxK0VDqJiIiIiIiInIWw5yeB30/GkOFGRxEJaiqdRERERERERM6Qee8e7G/NJatHT/yVKhsdRySoqXQSEREREREROUPhzz0DVisZgx8yOopI0FPpJCIiIiIiInIGLL/uJHTR22Te3Rd/mbJGxxEJeiqdRERERERERM5A2KTxBBxhZPQfZHQUkSJBpZOIiIiIiIjIaVi2bMb+/mIy772fQFyc0XFEigSVTiIiIiIiIiKnET7xKfyRUWTeP8DoKCJFhkonERERERERkf9g/fF7Qld+RGa/AQQio4yOI1JkqHQSERERERER+Q/hT4/DHxtLRp/7jY4iUqSodBIRERERERE5hZANX2H7bC0ZA4eC02l0HJEiRaWTiIiIiIiIyMkEAoRNGIuvTFkye/Y2Oo1IkaPSSUREREREROQkQj5bi+3r9WQMfggcDqPjiBQ5Kp1ERERERERE/i0QIPzpsfgqVSbr9juNTiNSJFmNDiAiIiIiIiISbGyrVhCy8UdSX5gOoaFGxxEpkjTSSUREREREROSf/H7Cnx6Ht1p1sm7ubnQakSJLI51ERERERERE/iH0w/exbttCysw5YNW3zSLnSiOdRERERERERP7i9RL2zFN469TFfd0NRqcRKdJU2YqIiIiIiIj8KfS9d7Du+pXk194Es8ZpiJwP/Q0SERERERERAcjOJnzS03guakR256uNTiNS5Gmkk4iIiIiIiAhgXzAfy/69pD3zLphMRscRKfI00klEREREREQkK4uw5yfiaXIZ2W3aGZ1GpFjQSCcREREREREp8RxzX8Vy5DCpM17RKCeRfKKRTiIiIiIiIlKypacT9sJzZDdvhefK5kanESk2VDqJiIiIiIhIieaY8zLmP46R/shjRkcRKVZUOomIiIiIiEiJZUpJJmzaZNztOuBtcpnRcUSKFZVOIiIiIiIiUmI5Zk7HnJRExsMa5SSS31Q6iYiIiIiISIlkSjiOY+Z03Fdfi/eiRkbHESl2VDqJiIiIiIhIiRQ2/UVM6Wmka5STSIFQ6SQiIiIiIiIljunoURyzZ+K+4WZ8tWobHUekWFLpJCIiIiIiIiVO2NTnITub9GGPGB1FpNhS6SQiIiIiIiIlivnQQRyvzyGr+x34q1U3Oo5IsaXSSUREREREREqUsOcnAZAxZLjBSUSKN5VOIiIiIiIiUmKY9/yGfcE8snr0xF+xktFxRIo1lU4iIiIiIiJSYoQ/9wyEhJAxaJjRUUSKPZVOIiIiIiIiUiJYdu4g9N2FZN7dF3+ZskbHESn2VDqJiIiIiIhIiRA2aQIBRxgZ/QcZHUWkRFDpJCIiIiIiIsWeZctm7EsXk3lvPwKxsUbHESkRVDqJiIiIiIhIsRf+zDj8kVFk3t/f6CgiJYZKJxERERERESnWrD98R+iqFWQ+MJBAZJTRcURKDJVOIiIiIiIiUqyFPz0Of1wcGffcZ3QUkRJFpZOIiIiIiIgUWyHr12H7/FMyBg4Bp9PoOCIlitXoACIiIiIiIiIFIhAg7Olx+MqWI/Ou3kanKTJW/HyQrb8n4/f6sZhNmE0mLOa8v/7eZj7xmD//bDbn3fbvx6c+xnziMf+4vvlf17CYTUa/ZXIKZ1Q6HT9+nNGjR/Pll1/i8/m44ooreOqpp4j9c8X/jz76iClTppCQkEDHjh15/PHHCQ0NzT1/+/btjB49mu3bt9OwYUMmTJhAuXLlcvcnJiYyZswYvvjiC8qWLcvIkSO57LLLcvf7/X5efPFFFi1ahNlspk+fPtx555359R6IiIiIiIhIMRTy2VpsX68n9ZnnweEwOk6R8Om2I7y54TeqlHJiNZnw+QP4AwG8/gB+fwCfP4DP78cX4M/H/jzHBALG5K5WJoJ+bWpRNlL/nYPJGZVOgwYN4uDBgwwePBifz8e0adMYMWIEL7/8MuvXr2fo0KHcdNNNtGvXjunTpzN+/HhGjx4N5BRWPXv2pHr16kyZMoVVq1bRt29flixZgtWa8/QDBgxg3759jB8/niNHjnD//fezdOlSKlWqBMDUqVOZPXs2w4YNo0KFCowcOZK4uDg6d+5cQG+LiIiIiIiIFGmBAOETxuCrVJms2zVo4UxsO5TE6+t20aBSNM/c2ZSE4+lnfQ1/4B/lVCDn97/Lqpxt/97v8/tztgX+Pu7EYwInHPPXtmyfn09/OcKoxRsZ2L4O9SpEF8C7I+fitKXThg0b2LJlCytWrKBs2bK52ydOnEh6ejovvvgiTZo0YcyYMQBUrVqVjh07MmDAAOLi4njjjTfw+XzMnDkTl8tFs2bNaN++PWvWrKFTp06sW7eO7777jvnz59OkSRMAduzYwauvvsqoUaNIS0tjzpw53HffffTs2ROAtLQ0pk+frtJJRERERERETsq28iNCftpIypQZYLMZHSfo/Z6UyZTV2ygT6WDAVXWwmM9tCWizyYTZYsJqyeeAp9Htiuo89uY3PLNsM3c2q8FV9coXbgA5qdN+FdWvX59FixblKZyio6Px+/34/X5+/vlnrr766tx9lSpVonr16nz99ddATmnVokULXC5XzhOazbRq1YoNGzbk7i9Tpkxu4QTQpk0b1q9fD8APP/yA2+3O8xytW7dm165dHD169Hxeu4iIiIiIiBRHfj/hT4/DW70G7ptuNTpN0Et3e3h2xRZMJhjasR5hoUVv+edy0WGM6taIiyrF8PqXu3j9y1/x+vxGxyrxTls6uVwuatSokWfbl19+SY0aNUhISMDv91OzZs08+ytWrMjevXsBOHr0KLVq1frP/RdeeOEJ+w8dOoTP5yM+Pp7Q0FCqVKmSuz8qKgqXy8X+/fvP+IWKiIiIiIhIyRD6wRKsv2wlY/ijYC16BUph8vr8TF39C8dSsxjUvi5livCaSGE2K0M61qNLw4p8vPUIEz/aQlqWx+hYJdpZj5fbtWsXK1asoGfPnmRlZQHkjmL6S1hYGImJiQC43e4T9oeHh+fZHxERccL5Ho+HlJQUsrKycLlcmEymE45JSEg42/giIiIiIiJSnHm9hE0cj7dOXdzXXm90mqAWCASY99VuthxK4u4WF1K7fJTRkc6b2Wyi++XV6Nu6JjuPJDNq8UYOJWYYHavEOqvK1+fzMWLECGrWrEm3bt04cOAAABZL3smaJpMpt5AKCQk5YT+Qu992krm1fxVMbrcbm82G+SRzSf/5HGcqNtZ5VscHs1KlXKc/KIgob8FS3oKlvAWrqOWFopdZeQuW8hYs5S1YylvwilrmYpH3jTdg16+wZAmlykQWfqj/EGzv79Lv9vLJtiPcdHk1bmx+4Qn7gy3v6fwz7w2lXNSuEsuYRT8w+v2fePT6xjSpUdrAdCcqau/vuTir0mnGjBns2LGDRYsWYbVaiY2NBSA+Pp6qVavmHpeUlJQ7HS42Npb4+Pg810lKSsLx5+0qY2Ji2L59+wn7ARwOB7GxsSQkJODz+fKUV8nJyYSFhZ1NfI4fT8PvN+j+jfmoVCkXx46lGh3jjClvwVLegqW8Bauo5YWil1l5C5byFizlLVjKW/CKWuZikTc7m5iRT+Jv2JikK9pAEL2eYHt/Nx1I4KVVW7m4aixdG1Q4IVuw5T2dk+UtbQ/hyesa8fzKrTzx9nfcfnk1OjSocMJMKiMUtff3VMxm038O8Dnj6XXr1q1jxowZjBgxIneNpoiICMqXL8/GjRtzjwsEAmzbto3SpXMaxNq1a+fZD7B169Y8+7dt24bb7c7dv2XLFux2OxEREdSqVQuv18umTZty9+/evZvMzMzca4iIiIiIiIjY35qHZf9e0kc8DkFQLASrQwnpTF3zC5ViwunXtjZmc/F9r+JcdkZe14hLqsYyf/1vzP5cC4wXpjMqnXbu3MmgQYPo0qUL3bt3z7OvQ4cOLFy4kJSUFABWrlzJH3/8wRVXXJG7f8OGDWzevBmAw4cPs3bt2tz9rVu3xu12s2DBAgA8Hg8LFy7k8ssvx2QyUalSJerVq8fs2bNzn3P+/PlERkZSr16983z5IiIiIiIiUixkZhL2/EQ8lzbF0/oqo9MErdRMD8+t3IrNYmZIx3rYQ05cDqe4sYdYGNi+LtddUpnPt//OhGWbSMnMNjpWiXDa6XUej4eBAwdis9m45ZZbcssjgAsuuIA+ffqwbNkybrzxRho1asSKFSto06YN9evXB3JKpaZNm3L33XfTpk0b1q9fT0xMDDfffDOQM72uX79+TJo0iR9//JEDBw7w66+/Mnbs2NznGT58OL1796ZHjx44HA4+//xzRowYgVV3IRARERERERHAMfdVLL8fIXXmHI1yOgWvz8+U1dtITHfz2DUNiXPZjY5UaMwmEzc2qUqF6DBe/nQnI9/byNBO9akUG250tGLttCOddu7cyZ49ezh+/Dh33HEHN954Y+6vrVu3Ehsby3vvvUejRo3YuXMnd911F5MnT84932QyMXPmTG677TZ27tzJpZdeyoIFC3A6/57z169fP8aNG0d8fDyRkZHMmzePiy66KHd/06ZNmT9/PjabjcTERMaNG0fPnj3z950QERERERGRoiktjbApz5PdojWeK5oZnSYoBQIBXv3iV7YfSaZPq1rUKBNx+pOKoctrlObxaxvi9QcY/f5P/LD3uNGRirXTDhWqV68eO3bs+M9jypQpw8SJE0+532azMXjwYAYPHnzKY7p160a3bt1Oub9x48bMmTPndHFFRERERESkhHG8+jLmP46R/shjRkcJWh/9fJAvdhzluksqc8WFJXt95OqlXYy5vjGTV23lhZVbuemyqnRtVCkoFhgvbs54IXERERERERGRYGNKSSZs2gu423fE+79LjY4TlH7Ye5y3v97DpdXiuP5/VYyOExRinKE8cW1DLqtRine+2ctLa3eQ7dUC4/lNiyKJiIiIiIhIkeV4aRrmpCQyHtYop5PZfzyNGR//QtVSTu5tXQuzRvPkslktPNC2NpWiw1n03V6OJmcyqENdosNDjY5WbGikk4iIiIiIiBRJpuPHccyagbvrdXgbNDQ6TtBJzsjmuRVbCbNZGdKxHqEl4E51Z8tkMnHtJZV5sENdDiakM3LxRvYcSzU6VrGh0klERERERESKpLDpUzBlpJOuUU4nyPb6mbxqK6lZHoZ0qqfRO6fR5II4Rl7XCLPJxNilP/PN7mNGRyoWVDqJiIiIiIhI0fP77zjmzMJ9w834atYyOk1QCQQCzP5sB7uOpnJ/m9pcUMpldKQioUqckzHXN6ZKrJOpa37hve/24g8EjI5VpKl0EhERERERkaJnwgTIziZ92CNGJwk6S3/cz/pdx7jp0qo0qRZndJwiJTLMxqPXXETzmmVY8sN+pq35hSyPz+hYRZYWEhcREREREZEixXzoIMycSdZtPfBfUM3oOEHl293HePe7fVx5YWmuaVzJ6DhFUojFTN/WNakYE8bbX+/haEoWQzrWJdZpNzpakaORTiIiIiIiIlKkhD0/EYCMwQ8ZnCS4/BafysxPd3BhmQh6t6yJSXeqO2cmk4kujSoxpFM9jiZnMvK9jew6mmJ0rCJHpZOIiIiIiIgUGSGfrcX+5ly47z78FTWS5y8JaW4mr9yKyx7CoI51sVn17X5+aFwllie7NSI0xMJTH/zMup1HjY5UpOirUERERERERIoE8/59RNzbC1+t2jB+vNFxgobb4+P5lVvJ9PgY1qk+kQ6b0ZGKlYox4Yzu1pjqpSOYuXYHb3+9RwuMnyGVTiIiIiIiIhL8MjOJ6HUH+AMkv/YmhIcbnSgo+AMBZn66g31/pPHAVbWpFKv3pSC4HCE8cnUDWtcpy7KfDjB55VYys71Gxwp6Kp1EREREREQkuAUCuB4ahHXLJlJfegV/tepGJwoa7323j+9++4Pul1ejcZVYo+MUa1aLmbtbXMidzarz8/4ERr//E/EpmUbHCmoqnURERERERCSo2V99Bfs7C8h4aATZV3UwOk7Q+GrnUZb+uJ+WtcvS6aIKRscpEUwmE+3rV2B4lwYkpGUzavFPbD+cZHSsoKXSSURERERERIKW9ZuvcT7xCO72HckYMtzoOEHj199TmP35TmqXi6RX8xq6U10hq18xmtHXN8JptzJh2WY+++WI0ZGCkkonERERERERCUrmo78T0bsHvspVSJ3+Mpj1LSzAH6lZTF61lejwUB7sUBerRe+LEcpFhfFkt8bULR/F7M9/Zf5Xu/H5tcD4P+krU0RERERERIJPdjYRd/fAnJZGymtvEoiMMjpRUMjM9vLciq14fX6GdaqPyx5idKQSLTzUyrDO9enQoAIrNx/i2RVbSHdrgfG/qHQSERERERGRoOMcOYKQ774hdcp0fHXqGh0nKPj9AWZ8sp1DiekMaFeX8tFhRkcSwGI20ePK6vRucSHbDiXx5JKN/J6kBcZBpZOIiIiIiIgEmdC338Tx6itk9BuI+9rrjY4TNN7+Zg8b9yXQ48oaNKgUbXQc+ZfWdcvxyNUNSM3yMGrJRrYcTDQ6kuFUOomIiIiIiEjQsG76CdfwwWQ3b0n6408aHSdofPbLET76+SDt6pWnXf3yRseRU6hTPoox1zcmOszGxOWbWbPlsNGRDKXSSURERERERIKC6fhxInrdgT82jpRZr4HVanSkoPDL4SRe+3IXDSpGc8eV1Y2OI6dROsLByG6NaFg5hjfW7eK1L37F6/MbHcsQKp1ERERERETEeD4fEffejTn+KCmvzScQF2d0oqBwNDmTKau2USbCTv92dbCYTUZHkjMQZrMyuEM9rm5UiU+2HWHi8s2kZnmMjlXoVDqJiIiIiIiI4cInjMX2xaekPfM83kYXGx0nKKS7vTy3YgsAQzrVJzxUI7+KErPZxK1NL+C+NrXY+XsKoxZv5FBihtGxCpVKJxERERERETGU7cOlhL34PJl39Sbrth5GxwkKPn+AaWt+4WhKFgPb16VspMPoSHKOmtUsw2PXNMTt8fHkko38vD/B6EiFRqWTiIiIiIiIGMayYzuugffjuaQJaeOeNjpO0Ji/fjebDybSq3kN6laIMjqOnKcLy0Yw+vrGlHbZeXbFFj78fq/RkQqFSicRERERERExhCklmYiet4HDQcqr8yA01OhIQWHNlsOs2XKYzg0r0qpOOaPjSD6Jc9l54rpGXFatFJ9sPmR0nEKhCaEiIiIiIiJS+Px+XP3vw7JvL8nvfYi/XHmjEwWFzQcSmffVLhpXieHWyy4wOo7kM3uIhf7t6hAX5+SPP9KMjlPgVDqJiIiIiIhIoQub8hyhK5eT9tQzeC6/0ug4QeFwYgZT12yjQnQ4/drWxqw71RVbJlPJ+G+r0klEREREgo514w+wdSOhzmh85Srgr1ABf5myEBJidDQRyQcha9cQ9vQ4sm64mcx77jM6TlBIzfLw7IotWC1mhnaqh8Omb9el6NNXsYiIiIgED78fx7QphE8YAz4fEf/YFTCZ8Jcuk1NAlauAr3x5/OUr4i9fPqeYKl8ef9lyYLMZFl9ETs+8dw8R9/XGV7c+qc+9CCVkxMd/8fr8vLh6Gwlpbh695iLiXHajI4nkC5VOIiIiIhIUTMeP4xpwL6Efrybrmm7Yp79Iwm+HMB85hOXwYcyHDmI+chjL4UNYft1ByGdrMafnXQ8jYDLhL1U6p4AqXzGnmPqrkKpQEV+5P4up4rxYsc+HKTkJc1IipsTEnD8nJmJKSszzO/YQQi9rRna7DgSiY4xOLSVFRgaRve4AIPm1+RAWZnAg4wUCAV7/che/HE7m/ja1qFk20uhIIvlGpZOIiIiIGM76zddE3NsL8x/HSH36ObJ63YO9dAS+0Eh8deriOcV5ptQUzIcOYT58CMuRw5gP//nnw4ew/LaLkC8/x5yacsJ5/lKl8ZWvgL9c+ZyRUn+OmPKXr5C73fBiyu3OLY5OLJAS8hZJuduTMCcn/edl/U4XgehocGcRMW8eAYsFT9MryO7YGXfHLvirVC2UlyclUCCAa8gALNu2kLzgXfxVtUg2wMpNh/hs++9ce3ElrqxZxug4IvlKpZOIiIiIGMfvxzFjKuFPPYm/YiWSlq/B27DxGZ8ecEXgqx2Br3ad/y6mjhzBfOjg38XUkcNYDh3Esm8PIRu+OmlR44+L+7uMKlc+p4z685evXE5Bhf00U2ACAUzpaXmLoxOKosQTC6SkREwZGae+rNlMIDoaf2RUzu+xcfiqX4g/OppAVDSBqCj8UdE5+6KiCUTH5PweGZm7Llap2HAS13yObeVHhK5cjvOJETifGIG3bn3cHTuT3akL3osaaeqT5BvH7JnYFy8ifcQTeNq0MzpOUNi47zhvbfiNJtXiuKFJVaPjiOQ7lU4iIiIiYghTwnFcA+4jdM0q3FdfS+oL0whE5P+0koArAp8rAl/NWqcuptJSc4qpf4yUMh8+nDO1b98+Qr5ejzkp6YTz/LGxuetJUSqWiPg//i6QknJGHpm83lNnCw3FHx2TWw75ql6APyoqpzjKLYz+US79+TjgdIHZfH5vjNmM9+L/4b34f2Q8OhLzb7sJXfkRtpXLCXvhWcKfn4ivfAWyO3TC3elqPFc003pZcs5CNnxF+MhHcXfsQsaDQ42OExQOJKQz/ePtVIlzcm/rWphV8EoxpNJJRERERAqd9btviOjbC3P8UVInTCLr7r6GjqgJOF34LnThu7DmqQ9KTz9hCl9uMXXoEPy6A7MrkkBUNL4KFQlEReeMPPpXYfTPIgmHo9Be4+n4q1Uns98AMvsNwPTHH9g+XkXoR8uwv/0mjtdm43dFkH1VO7I7diG7bbsCKQileDIfOUxE7zvxVb2A1Gkzz78wLQaSM7N57qMt2EMsDOlYD3uIxehIIgVCpZOIiIiIFJ5AAMdL0wgfNwp/+Yo50+kaXWx0qjMTHo6vxoX4alx40t2lSrlIOpZayKEKRiAuDvett+O+9XbIyMD2xWfYVi4ndPUK7EveIxASgufK5rg7diG7Y+ecqYYiJ+N2E3F3D8jMJGXJcpWVQLbXzwsrt5GS5eHxaxoS4yzGNzaQEk+lk4iIiIgUClNiAq6B9xO6agXuzl1JnTKdQGSU0bHkdMLCyO7YmeyOnUnz+bB+/x2hK5ZhW7kc1yND4ZGheBo2zlmIvNPV+OrU1TpQksv52MOE/PAdyXPm4atV2+g4hgsEArz6xU5+PZrCgHZ1qFbaZXQkkQKlcY0iIiIiUuCs339LdNvm2NZ+TNpTz5Dy2nwVTkWRxYL3sqakPzmOxA0/krDuO9IefxKsVsKfeYqYVpcT06Qh4U88QshXX8J/rGclxZ/9zbk45r5KxoDBZHe91ug4QeHDjQdYtzOeG5pU4bLqpYyOI1LgNNJJRERERApOIIBj1nTCx4zEX74CSR+uwnvx/4xOJfnBZMJXsxaZNWuROXAI5qO/Y1u1AtvK5Then0PYrBn4o6PJbtcxZxpeqzbgdBqdWgqJdeMPOB8ZSnaL1qQ/OtLoOEFh3S9HeOfbvVxRoxTXXVzZ6DgihUKlk4iIiIgUCFNSIq6B/QhduRx3p6tzptNFRRsdSwqIv0xZsu7sRdadvSAtDdunnxC6cjm21Suwv7OAQGgo2S1akd2xC+72nQiUKWN0ZCkgpj/+IOLuHvhLlyFl1qtg0SLZe46lMvGDTVQv7eKeVrUwaQqqlBAqnUREREQk31l//J6IPj0xHzlM2tgJZPbtp3V+ShKnk+yu1+ZMqfJ4CPlmQ85C5Cs/InTNKpwmE95LmuSMgOrU5b/vGihFi9dLxL29MB//g6RlqwnExhqdyHA7f0/mhZXbiHCEMLhjPWxWrXIjJYe+2kVEREQk/wQCOF6eQVTXDgAkfbiKzHsfUOFUkoWE4GnWgvRxz5Dw3SYSPl1PxvBHITsb57hRxFz5P6Ivv5jw0U9g/fYb8PmMTiznIXzck9i+/JzUiZPxXtTI6DiG+2LH74z/YBMOm4UJt19GVJjN6EgihUojnUREREQkX5iSk3A9+AChH32Iu2NnUqfMIBAdY3QsCSYmE7569cmoV5+MoQ9jPnQQ28qPCF25HMes6YRNn4I/rhTuDp3I7tiF7BatwOEwOrWcodCliwmb8SKZd/fBfevtRscxlN8fYOE3e1j+80HqVohiYLs6VIpzcuxYqtHRRAqVSicREREROW/WjT8Q0acX5sMHSRs9nsz7NLpJTs9foSJZvfuS1bsvpuQkbJ+syZmG98H7ON6cSyAsjOxWbXF37AzdbwRCjY4sp2D5ZRuuBx/A0+Qy0sZMMDqOoTKzvcz4ZDsb9yXQtm45elxZHatFk4ykZNJXvoiIiIicu0AA++yZRF3dHvw+kj5YSeb9/VU4yVkLREbhvv4mUl9+neO//EbSwiVk3XJbTqE58H4oV47wxx/GlJhgdFT5F1NyEhE9b8PvdJIyZy7YSu4UsviUTEa//xM/70/grmY16NXiQhVOUqJppJOIiIgYz+sl7PmJUCoayxWt8dWspdKiCDClJOMa1J/QZUtxt+9I6tSZmk4n+cNmw9O6LZ7WbeHp57D+vJHod+bjmD0L+zsLyBj6MJm9+pTociNo+P24HuiL5cB+khYvx1+2nNGJDLP9SDJTVm3D5w8wvEsD6lfU3TpFVDqJiIiIsXw+XP3vxb54EQAxgK9yVdztO5DdriOeK5pBqKbUBBvrzxuJuOcuzAcPkDZqHJn9BqgolIJhMuFtdDG0a0niHb1xjnoU5xMjsL/6Cukjx5Ld+Wp97Rko7LlnCF29ktQJz+JternRcQzz+fbfefWLXynlsjO0Uz3KRYUZHUkkKGicn4iIiBjH58P1YD/sixeR9tgoOHCA1Gen4K1dG8f8N4i6pRtxtaoS0fN27G/Nw3T0qNGJJRDAPudlorq0A6+XpKUryXxgoL7pl0Lhq1uP5HfeJ3nBuxASQmSv24m8rjPWnzcaHa1Esq1eQfikCWTd3J2su/sYHccQfn+AN9fv5pXPdlKnfCSjr2+kwknkHzTSSURERIzh9+McOhD7OwtIH/4omQ8OxVnKRdadvci6sxdkZGD76gtsq1dhW7OS0I8+xAV4Gl9MdruOZLfviLdBQ5UdhciUkoxzyEDsHyzBfVV7UqfNIhATa3QsKWlMJrLbtie7ZRvs814nfNJ4otu1JOvm7qQ/OhJ/+QpGJywRzL/txtWvL576F5E66YUS+f/ijGwv0z/ezs/7E2hfvzy3X1Edi7nkvQ8i/0UjnURERKTwBQI4hw/B8dY80oc8RMawR048JiyM7HYdSZs0mYSN20hY+xXpI54As4WwSROIvqoFMQ1r4xz6ILZVKyAjo/BfRwli3fQT0Ve1IHT5B6SNHEvK/HdUOImxrFayet1DwtcbyRgwmND33yPm8osJe+YpSEszOl3xlpZGZK/bwWIm5fU3weEwOlGhi0/JZPSSn9hyMJFeLWpwZ7MaKpxETkKlk4iIiBSuQADniGE45r5KxoDBZDz8+OnPMZnw1W9AxuCHSFrxCce37CLlxZfwNrmM0CXvEtnjFuJqVyXithuxvzYb88EDBf86SopAAPurrxDV+Spwu0l6fwWZ/R8Esz5GSnAIRESS/sRoEr76HneHToQ/9wwxl1+M/a154PMZHa/4CQRwDemPZcd2Uma9hr9yFaMTFbpfDicxcvFGkjKyebhLA9rWLW90JJGgpU8LIiIiUngCAcJHjsDx6itk3D+A9MefPKcpGYFSpXDfejspc+ZyfPsekt79gMy77sa661dcDw8h9uJ6RLe6grDxY7B+942+8TxHptQUXH174XpkKNnNW5K49iu8lzU1OpbISfmrVCX15ddJXL4Gf8VKuAY9QPRVLQj54jOjoxUrjpnTsb+/mPRHR+Jp1cboOIXu021HeHrZZiLsIYy+vjF1K0QZHUkkqKl0EhERkcIRCBA+ZiRhs2aQ0fd+0p8clz9rgNhseFq0In3s0yR88xMJ638gbdQ4/FFRhE2dTHSXdsTWr4Gr/73YPliCKSX5/J+zBLBs3kTUVS0IXbaUtMefJOXNRQRiNZ1Ogp+3yWUkffQxKS+/hiklmagbryGixy1Ydv1qdLQiL2TdF4SPeQJ3565kDhhsdJxC5fMHmPfVbuZ88Sv1KkTxZLfGlI0sedMKRc6WFhIXERGRghcIEDZhLGHTp5DZ6x7Sxz5dMIvOmkz4alxIZo0LyXxgIKakRGyffoJt9Upsa1Zif2cBAasVz+VXkt2uA9ntO+KrViP/cxRlgQD2ua/hfPxh/DGxJC9ZjqfpFUanEjk7JhPu627A3bELjpdfIuyFZ4lucRlZd91N+rARKlDPgfnQQSL69sRXrTqpU18qUQuHZ7i9TP34FzYfSKRjgwp0v7ya1m8SOUMa6SQiIiIFLmzSBMJfeJbMHj1Jm/BsoX2zEoiKxt3tRlJfms3xrbtJ/GAVmfcPwHwsHufIR4lpejHRV1xC+KjHCPnqS/B4CiVXsDKlpeK6vzeuhwbhuaIZiZ+sU+EkRZvdTubAwSR88xNZt9+F/bXZxFzWCMeMqeB2G52u6MjKIuLuOyDLTcrrbxFwRRidqND8npzJk0s2su1QEr1bXMgdV+oOdSJnQ6WTiIiIFKiwyZMIf/ZpMrvfQdqkF4xbgNpqxdv0ctKfGE3iF99w/LtNpE6YhL9SZRxzZhHVrQuxdarh6tuT0EVvYzp+3JicBrFs2Zwzne79xaQ9NorkBe8RiIszOpZIvgiUKkXapMkkfrYBT5NLcT75GDHNmmD7cCkEAkbHC3rORx8iZOOPpE6die/CmkbHKTRbDyUyavFGUrI8PHJ1A1rXLWd0JJEiR6WTiIiIFBjH1BcInzCWrJtuJe35qUF1xzN/lapk9b6X5IVL+GP7XpJffwt312uxfbWOiAf6EluvOlFXt8fx4vNYftlWfL8xDQSwz3ud6M5tMaWnk7xkOZkPDg2q/1Yi+cVXuw4pC94j6e3FBMLCiOzdg6hrOmLd+IPR0YKWfe5rOOa/QfqgYWR36Wp0nELzydbDTFy+hagwG2Oub0yd8lFGRxIpks7q08S6deu46qqr8myLj4+nVq1aJ/zat29f7jHbt2+ne/fuNG7cmJ49e3LkyJE810hMTGTw4MFccskldOnShW+++SbPfr/fzwsvvMCVV15J8+bNmTt37tm+ThERESlkjpnTcI4dSVa3G0h98SWwWIyOdGpOJ9mdryZt8jSOb95J4urPyBj8EGRl4Rz3JDEtmxLzvwY4HxlKyNo1kJVldOL8kZaG6/57cA0diKfpFSSu/QrP5VcanUqkwHnaXEXiJ+tIfe5FLLt3Ed2hNa7778F88IDR0YKK9ftvcY4YRnbrtmQ8/JjRcQqFzx/gjXW7eO3LXdSvGM2obo0oHaEFw0XO1RkvJL57926GDh1KeHh4nu3btm0jJiaGl19+Oc/2cuVyhh4eP36cnj17Ur16daZMmcKqVavo27cvS5YswWrNefoBAwawb98+xo8fz5EjR7j//vtZunQplSpVAmDq1KnMnj2bYcOGUaFCBUaOHElcXBydO3c+rxcvIiIiBcM+eybOkY/i7nodqdNfCe7C6d/MZryNLsbb6GIyHn4M85HD2D5enbMQ+YL5OF59hUBYGNktWkPTJthDwwlEReOPjiEQE4M/KppATEzOmidBvNCuZesWIvrcheW33aSPeIIMjW6SksZqJatHT9zXXY9j6guEvTSV0OUfkHF/fzIHDCbgdBmd0FCm+Hgiet+Jv1wFUmbOKVr/Hz9H6W4PU1f/wpZDSXRuWJFbL7sAs9ZvEjkvZ1Q6bdq0iT59+lCpUiUSEhLy7Nu2bRu1atWiQYMGJz33jTfewOfzMXPmTFwuF82aNaN9+/asWbOGTp06sW7dOr777jvmz59PkyZNANixYwevvvoqo0aNIi0tjTlz5nDffffRs2dPANLS0pg+fbpKJxERkSBkf30OrkeH4+50dc43KtaifbNcf7nyZPXoSVaPnpCZiW39lzl3w/t4Naxczqm+LQ1YLASi/yyjoqLxx8QQiP67lPJHx+CPjs7ZFh2TeyxhYQX7ggIB7G/OxfnoQ/gjIkl+70M8VzYv2OcUCWIBVwQZj44kq0dPwp8aTfjkZ3HMn0v6I4+TdVuPElG2nMDjIaLPXZgTE0hc/jGB6BijExW4I0kZPLdiK8dSs+jTqiYta5c1OpJIsXBGnwK/++47Hn74YQCmTZuWZ9+2bduoX7/+Kc/dsGEDLVq0wOXK+UhmNptp1aoVGzZsoFOnTmzYsIEyZcrkFk4Abdq04dlnnwXghx9+wO12c/XVV+fub926NY888ghHjx6lTJkyZ/hSRUREpKDZ57+Ba/hg3O07kvLK6xASYnSk/OVwkN22Pdlt2wNQKtrBH78ewJyUiCkhAXNiAqbEBMyJif/6PQHzoUOYt2zOOSYj45RPEbDbc4qp6JicoupkhdU/i6uoaALR0WCznT5/WhquB/pif3ch2S1akzLjFQKlS+fXuyNSpPkrVSZ15hwy+96Pc+SjuIYOxDF7Fmmjn8LTqo3R8QpV+JgnsG34ipTpL+NrcJHRcQrcloOJvLj6FyxmEyO6XkTtcpFGRxIpNs6odOrVqxdms5nFixefsG/btm3s37+fd999F7fbzWWXXcYjjzxC1apVATh69Cjt2rXLc07FihVZu3Zt7v4LL7zwhP2HDh3C5/MRHx9PaGgoVapUyd0fFRWFy+Vi//79Kp1ERESCROjbb+IcOpDsNleRMmfemZUgRZ3VSiAuDt/Z3uUtK+vvoipPYZV4QnFl2bUT61/bPZ5TXtLvdP09sio6+oRRVAFXBMycSujOnaQ//BgZg4aVzBEcIqfhvfh/JH24CtuypThHjyTq5utwX9We9FHj8NWqbXS8Ahf63juEzZpBRp/7cN90q9FxCtyaLYeZ99UuykeHMaRjPa3fJJLPzqh0Mp9ifn9CQgKHDh2idevWDBkyhKysLKZMmUKfPn1YuXIlFosFt9udO8rpL+Hh4SQmJgLgdruJiIjIsz8sLAyPx0NKSgpZWVm4XC5M/1oTISws7ISpfiIiImKM0EVv43qwH57mrUh+7U0IDTU6UnCz2/GXLQdly+E703MCAUzpaX8XU/8urJISMSf8XVhZD+z/c3sSpr/uvFemDMnvfoCnWYuCemUixYPJRHbX60ho3wnH7FmETZ5EdKvLybqzF+kPPUrgbIvmomLTJlxDBpDd9ArSn3zK6DQFyuvzM++r3Xyy7QiNq8TQr21tHLaiPR1cJBid198qp9PJokWLqF+/fm4xVadOHTp06MC6deto2bIlISEhWE7yU7SsP+/6YjvJT0H/Kpjcbjc2m+2kpZfJZMq9xpmKjXWe1fHBrFSporWwofIWLOUtWMpbsIpaXih6mQs879tvw4D7oFUrbMuWUeo81yTS+/tfIuCC8md3it8PSUlw/DiUK0eUs2h9HtLXQ8FS3tNxwZOPQf97YfRoHC+9hOO9d+Cxx2DgQLDbT3uFIvMeJyRAt26YoqKwLXmPUmWLxjpO5/L+pmRm8+y7P/LT3uPcdHk1erWpjaWQFgwvMl8Pf1LeglXU8p6L8yqdbDYbF12Ud45vlSpViI2NZfv27bRs2ZLY2Fji4+PzHJOUlITDkTNsMSYmhu3bt5+wH8DhcBAbG0tCQgI+ny9PeZWcnEzYWX6oPX48Db8/cFbnBKNSpVwcO5ZqdIwzprwFS3kLlvIWrKKWF4pe5oLOa/vwfSL69sJzaVOSX30L0n2Qfu7Pp/e3oIRAVFlKOZ1FJG+OovP+5lDegmVs3lAYOR7LrXcRPuYJQh9+GN/0GaQ/MRr3Nd1OeafKoH2PMzMxxx/FfPQo5qO/Y47/ndClS7AdOEDi+x/htYRDMOb+l3N5fw8nZvDcii0cT3PTt3VNWtQqS8LxtAJKmFfQfj2cgvIWrKKW91TMZtN/DvA5r9Lp+PHj7N+/n8aNG+du83g8pKWl4Xa7AahduzYbN27Mc97WrVsp/eeilbVr12bx4sW43W5C/xyKv2XLFux2OxEREdSqVQuv18umTZtyn2f37t1kZmbmXkNEREQKn23FciLuvRvvxf8j5a1FEB5udCQRkQLlq1mLlPnvEPL5pzhHPUZEn554Zs0gbcx4vP+71NhwgQCm5KScIin+zzLpH6WSOT7+720pySeebrPBSy/hbXKZAeELx6YDCUxb8wtWi5lHr7mImmW1YLhIQTuv0mndunVMmjSJjz/+GPufQ0tXrlxJVlYWDRs2BKBDhw7079+fzZs306BBAw4fPszatWsZNGgQkHMnulGjRrFgwQJ69uyJx+Nh4cKFXH755ZhMJipVqkS9evWYPXs206dPB2D+/PlERkZSr16984kvIiIi58i2egUR99yJ96KGJL/9HgFn8R8eLiLyF0/L1iR+8iX2hW8RNn4M0Z2vIqvbDaQ/9iT+ylVOf4Gz4fNh/uPYn4XRP8ujf5RKx3K2mf78wf8/Bex2/KXL4i9TBl+tOnhatMJfugz+Mn9uK10Wf+kyBOLiKFU2qkiMcDpbgUCAVZsP8+aG3VSKCWdIx3rEuU4/NVJEzt95lU5t27Zl8uTJ9O3bl65du3L48GHmzJlDo0aNaN68OZBTKjVt2pS7776bNm3asH79emJiYrj55puBnOl1/fr1Y9KkSfz4448cOHCAX3/9lbFjx+Y+z/Dhw+nduzc9evTA4XDw+eefM2LECKxWLfQmIiJS2ELWriHi7h5469YneeGSnLuiiYiUNBYLWbf1IOuaboRNe4Gwl6YS+tEyMu99gIwHh5z+/40nmeKW5/HRo1iO/o7p+B+Y/P4TTvdHReUUR6XL4mly2Z8lUk6R9M9SKeCKOOX0v5LA6/PzxrpdfPrL71xSNZb729bGHqI7d4oUlvNeSPyVV15hzJgxjB07lujoaHr16kW/fv1yF/82mUzMnDmT6dOn88UXX3DppZcybNgwnP9YxLJfv36UK1eOhQsXEhkZybx58/KsFdW0aVPmz5/PtGnTSExMZNy4cdx0003nE11ERETOQcjnnxJ51214a9Ym+Z0lBCKjjI4kImIsp5OMRx4nq0dPwsePIezF57G/NZeMwQ9BlQo4ft175lPcLBb8pUrnlEblyuFt1Djnz/8okfxlyuIvVfqMFjEv6VKzPExZtY3tR5Lp2rgSN11aFXMJLuBEjGAKBAJFf2XtM6SFxI2hvAVLeQuW8hasopYXil7m/Mwb8tWXRN52I76q1UhavIxAbGy+XPefSvL7WxiUt2Apb8EqKnmtP/1I+KjHsG34KndbwOH4uzgqXSa3PPKVKUugdOmcKW5lyub8f/Ukd/4uLEXlPf7Lf+U9lJDOcyu3kpju5p6WNbmyZplCTnei4vT+BiPlNUaBLiQuIiIiJUPI1+uJvP0mfJWrkPTuBwVSOImIFAfeRheT/P5HWLZuIaZ8LH9Yw0v8FLfC9tO+BKZ9/AuhVjOPXdOQGmU0DVzEKCqdRERE5D9Zv/2GiO434itfgaR3PyRQqpTRkUREgpvJhK9+AyjlIlAMRjIUFYFAgBWbDrHg69+oHBPOkE71iHVqGqKIkVQ6iYiIyClZf/yeyFuvx1+mDMmLlxEoY/z0BBERkX/z+vy89uWvfL79KE0uiOPeNrW0YLhIEFDpVMSYf9sNsRed/kAREZHzZP15I5E3dyMQG0vy4mX4y5YzOpKIiMgJUjKzmbJ6GzuOpHDtxZW5oUkVLRguEiTMRgeQsxAIENPqchg71ugkIiJSzFk2byLypmsJREaStHgZ/vIVjI4kIiJyggMJ6YxavJHf4lPp17a27lAnEmRUOhUlJhPZLVvDjBngdhudRkREiinLtq1E3XQNgXBnTuFUqbLRkURERE7w9c6jjF7yE9m+AI9f05ArLixtdCQR+ReVTkVMZu97IT6e0KWLjY4iIiLFkGXHdqJu7ErAFkrSex/ir1LV6EgiIiIn+Ojngzy58HvKRjoYc31jqusOdSJBSaVTEeNp2Rrq1MExeyYEAkbHERGRYsSy61eirr+agNlC8pJl+KtVNzqSiIjICT7eepi3NvzGlbXL8sS1DYl1hhodSUROQaVTUWMyQf/+hPy0EesP3xmdRkREignLb7uI7NYFAgGSFy/DV/1CoyOJiIic4Ie9x3lj3S4aVY7h0RsaE6o71IkENZVORdGdd+J3ReSMdhIRETlP5r17iLy+Kyavh6T3PsRXs5bRkURERE6w62gK0z/+hapxTvq3q4PFrG9nRYKd/pYWRU4nWbfdQegH72P+/YjRaUREpAgz799H1PVXY8rMIGnRB/jq1DU6koiIyAl+T87kuRVbiQqzMaxTfewa4SRSJKh0KqIye/UBnw/7G68aHUVERIoo86GDRF3fFVNqKsmLluKr38DoSCIiIidIycxm0vLNBAjwUOf6RIbZjI4kImdIpVMR5a9Wneyr2uOY+xq43UbHERGRIsZ85DBR3bpgSkwg+Z0leC9qZHQkERGRE7g9Pp5bsZWE9GyGdqxPuagwoyOJyFlQ6VSEZd5zH+Zj8YR+sMToKCIiUoSYj/5O5PVXYzp2jOS338Pb+BKjI4mIiJzA7w8w/ZPt/BafSr+ranNh2QijI4nIWVLpVIR5WrbGW+NCHHNmGR1FRESKCFN8PJE3dMVy5AjJC97D2+QyoyOJiIicIBAI8Ma6Xfy49zg9mlWnyQVxRkcSkXOg0qkoM5vJ7H0vIT/+gPWH74xOIyIiQc50/DhRN12D5cB+kt9ahLfp5UZHEhEROallPx3kk21H6NKwIu3rVzA6joicI5VORZz7lu74nS4cszXaSURETs2UmEDUjddg2fMbyfMW4rmimdGRRERETmr9r/Es/GYPTWuU4pamFxgdR0TOg0qnIi7gdJF12x2EfrAE09GjRscREZEgZEpKJPKm67D8uoPk19/C06KV0ZFEREROatuhJGZ9uoPa5SK5t3UtzCaT0ZFE5DyodCoGsu7ug8njwTH3VaOjiIhIsElOJvKWblh/2UrK62/iaXOV0YlERERO6sDxdCav2kq5SAeDO9YjxKJvV0WKOv0tLgZ81Wrgvqo99jdehexso+OIiEiQMKWlQqdOWDdvImXOPLKv6mB0JBERkZM6nuZm0kebsVstDOtcn/BQq9GRRCQfqHQqJjLvuRdL/FFCP3zf6CgiIhIETKkpRHa/Eb79lpSXXye7Y2ejI4mIiJxUhtvLsx9tISPbx7DO9Ylz2Y2OJCL5RKVTMeFp1RZv9Ro4Zs80OoqIiBjMdPw4kTd0xfr9t/DWW2RffY3RkURERE7K6/MzZfU2Didl8GD7ulSJcxodSUTykUqn4sJsJrN3X0J++B7rj98bnUZERAxiPnKYqOs6Yf1lGymvvwU332x0JBERkZMKBAK88tlOth5K4p6WNWlQKdroSCKSz1Q6FSPuW27D73ThmD3L6CgiImIA8949RHXtiPngQZLfXkx2h05GRxIRETmlRd/u5atf47mpSVWa1ypjdBwRKQAqnYqRgCuCrFtvI3TpYkxHjxodR0RECpHll21Ede2AKTWZ5MUf4rmyudGRRERETumTrYf5YOMBWtcpyzUXVzI6jogUEJVOxUxW776YPB4c814zOoqIiBQS64/fE3VdzqimpKUr8Ta+xOBEIiIip/bj3uO8vm4XjSrH0LP5hZhMJqMjiUgBUelUzPiqX0h2m6uwv/EqZGcbHUdERApYyLoviLzhGgKuSJI+XIWvdh2jI4mIiJzS7qMpTPv4F6rGOenfrg4WswonkeJMpVMxlNnnPixHfyd02VKjo4iISAGyrVpBZPcb8FesSNKyVfirXmB0JBERkVM6mpzJcyu2EumwMaxTfewhFqMjiUgBU+lUDGW3vgpvtepaUFxEpBgLfe8dInrehrdOXZKWrsBftpzRkURERE4pJTObics34yfA8C71iQyzGR1JRAqBSqfiyGwmq3dfQr7/FutPPxqdRkRE8pn9tdm4+vXBc9nlJL/3IYGYWKMjiYiInJLb4+P5lVtJSM9mSMd6lIsKMzqSiBQSlU7FVNatt+MPd2q0k4hIMeN48XlcDw8hu10Hkhe8R8AVYXQkERGRU/L7A8z4ZDu7j6bS76ra1CwbaXQkESlEKp2KqYArAvettxH6/nuYjh0zOo6IiJyvQIDwsaNwjnuSrOtvJOW1N8HhMDqViIjIKQUCAeZ9tZsf9h6nR7PqNLkgzuhIIlLIVDoVY5m978WUnY1j3mtGRxERkfPh9+McPoSwqZPJvKs3qdNfgZAQo1OJiIj8p+U/H2TN1sN0bliR9vUrGB1HRAxgNTqAFBxfjQvJbt0W++tzyBgwWN+giIgURR4PrgH3YV+8iIwBg0l//Ekw6fbSInLm/IEAqVkektKzScrI+ZWY7sYWGkKlSAe1ykVis+pn0ZK/1v8az9tf76Fp9VLc2lR3VxUpqVQ6FXOZ99xL5O03E7r8A9zX3WB0HBERORuZmUT0uYvQ1StJe/xJMgcOMTqRiAQRvz9AcubfRdJfpVLiPx4nZrhJyfTg8wdOON9sAn8AbFYztctFclGlaBpUiqF8lAOTym05D9sOJTHr0x3ULhfJvW1qYdbXk0iJpdKpmMtu2x5f1QtwvDJTpZOISBFiSkslosethKxfR+ozz5PV6x6jI4lIIfH6/CRnev5RJrn/MULp75IpOTObwIldEi57CFFhNqLCbFSICSMqzEZ0mI2ocFvu9qiwUGJjw/li00E2H0hk04FE5q//DfiNWGcoDSpGc1GlaOpVjCY8VN8yyJk7kJDOC6u2UjbSweCOdQmxaBSdSEmmf0GKO7OZzN59cT4xAuvPG/E2bGx0IhEROQ1TwnEiu9+AddPPpE5/GfeNtxgdSUTygcfnJznjH6OR0rNJynCfUCalZnr4d5dkAiIcf5dJVeKcuX+OzlMm2bCe4Tf5dpuVxlViaVwlFoBjKVlsOpjA5gOJfPPbMT7b/jsmE9QoHUGDSjklVLVSLsxmjVqRk0tIczNp+WZsVgsPda5PeKiW9xAp6VQ6lQBZ3e8gfMI4HLNnkTp1ptFxRETkP5h/P0LkTddi2buHlNffIrtDJ6MjiRjC7fGx+/cUEhPT8/3aJxkcdOpjTzaU6BTiszzsPZT0r1IpZ4pbUkY2aVneE84xmSDKkTMKKdYZSvXSrpzyKDxnNFL0n0VSZJgNSwGXPaUi7LStW562dcvj9fnZHZ/KpgOJbD6QyJLv97H4+32Eh1qpVyEqdyperDO0QDNJ0ZGR7eXZFVvIyPbxxLUNiXPZjY4kIkFApVMJEIiIJOuW7tjfnEvayLEESpUyOpKIiJyEee8eom68FtPxP0he8B6eZi2MjiRS6Hz+AJ9v/533vt9Hcka20XHOmcVsyh15VCbCQa2ykXmmt0WHhxIVZiPCHhKUI4esFjO1ykVSq1wkN11aldRMD1sOJeaWUN/+9gcAFaLDckdB1S4Xic1qMTi5GMHr8/Piqm0cSsxgWKf6VIlzGh1JRIKESqcSIrP3vThem41j/utkDH7I6DgiIvIvlu2/EHnTtZiy3SS/9wHei/9ndCSRQhUIBPhpfwILvt7D4cQMapaN4P4Odck8i+LpbKqbs6p5znAR5LiYcEweL1FhNpz2kGK1eLLLEcLlNUpzeY3SBAIBDiZksOlAzlS8T7YeZuWmQ4RYchYk/6uEqhAdpgXJS4BAIMDsz3ey5VASfVvXpEGlaKMjiUgQUelUQvhq1iK7ZWvsr88ho/8gCNH8ahGRYGHd+AORt15PwBZK0vsr8NWpa3QkkUL1W3wqC77+jV8OJ1M20sGDHeryv6qxlC4dwbFjqUbHO2OlSrmKVN5zZTKZqBQbTqXYcLo0qoTb42P7keQ/R0El8NaG33hrA8SE22hQKZoGFWOoXzEKp12fP4ujd7/by7qd8dzYpAotapU1Oo6IBBmVTiVIZp/7iLzjFkI/+hD3tdcbHUdERICQr74k4o5bCMTGkbToffwXVDM6kkihOZaSxaJv97B+1zEi7CHc1awGreuUPeOFsCU4hIZYaFg5hoaVY4Dq/JGalXNHvIOJfPfbH3y+/SgmE1Qr5fpzLahoqpeOKPA1qqTgfbLtMEt/PEDrOmW59uLKRscRkSCk0qkEyW7bHl+Vqjhmz1LpJCISBGyrVxDR+058VS8g+Z338Zcrb3QkkUKR7vaw9McDrN58CJPJxDWNK3F140qE2fTRtDiIc9lpXbccreuWw+cPsDs+JaeEOpDI+z/uZ8kP+wmzWalXMYqLKuaUUFp0uujZuO84r3+5i4aVY+jZ/EJNpRSRk9K/7CWJxUJm7744Rz6KdfPPeBs0NDqRiEiJFbp4Ea7+9+Kt34DkBYsJxMYaHUmkwHl8fj7ecpj3f9xPhttLs1pluLFJVd0BrRizmE3ULBtJzbKR3NCkKmlZHrYcTGLzwQQ2HcgZCQVQPspBg0oxuQuSh4ZoQfJgtjs+lWlrfqFqnJMB7epo1JqInJJKpxImq/sdhD89DvvsWaRNmWF0HBGREsn++hycDw/Bc/mVpMx7m4ArwuhIQSE100NoRjb+QKBYLcAsOQsNf7P7GAu/2cux1CwaVIzm1qYX6A5XJZDTHkLTGqVoWqMUgUCAQ4kZuXfEW7vtCKs2HyLEYqJW2b8WJI+hYowWJA8mR5Mzee6jLUQ4bAzrVB+7CkIR+Q8qnUqYQGQUWTd3x75gPulPjCEQF2d0JBGREsXx4mSc40bhbteBlNlzweEwOpIh3B4fe/5I47f4VHYdTeG3+FT+SHMDYDaByx5ChMOGyxFChCOECHvIP/5sI8KR8zjSEUKYzapvSIPY9iPJLNjwG7vjU6kcG87DXerToFKM0bEkCJhMJirGhFMxJpzODSuS7f17QfItBxJZ8PUeFny9h+gwG/UrRdO8XnlqxIRjs2rNL6OkZnqY9NEW/IEAD3WpT2SYzehIIhLkVDoVMVsOJnJpxPnNec/sfS+O1+dgf/MNMh8cmk/JRETkPwUChD81mrAXnyer2w2kTnu5xNxJ1O8PcCgpg91HU9gdn8ru+FQOJqTjD+Tsj3OGUr1MBO3qO4mMcHD4WCqpWR5SMnN+7T2WRkpmNhnZvpNe32I2/VlSheT5PfJfpdVfJVaYzaKSqhAcTspg4dd7+GHvcaLDbfRtVZNmNctg1jQcOQWb1cJFlWK46M9S8niamy0Hc9aC+nHvcb7ccZQIewit65bjqnrliA7XtMzC5Pb4eG7lFo6nuRnRtQHlo8KMjiQiRYBKpyLmhVXbaLTjKA+0qXXOH5h9tWqT3aI1jtdmk/nAg2DVl4GISIHy+3E+PBTHG3PIvPNu0p55DizFdzrC8TQ3v8Wnsjs+p2TacyyNLE9OYRRms1CttIuujStTvbSL6qVdeX5S/l+3nPf4/KRmev4spLJzS6mULA+p//h9d3xOaZX5HyVV3pFTpxpVZSPCHoJDJdVZSc7IZvH3+/j0lyPYrBZuurQqHRtU0Bo9ctZinaG0rF2WlrXL5pTX6dm8s+5XPvhxP8t+OsCl1eLo0KACNcpoinJB8/sDzPhkO7uPpjKwfV1qlo00OpKIFBFqG4qYG/5XhTc3/EbjStFcWbPMOV8n8557ibzzVmwrlpHd9br8CygiInl5PLgG3Id98SIy+g8i/YnRUIwKjIxsL3uO5Yxe2n00ld/iU0nMyAZyyp0qsU6a1yxD9TIuqpV2UTbScc7rNYVYzMQ4Q4k5w0WnPT7/n8VUdp6RU/8ureJTskjJ9OQWY/9mNZvylFB/Te2LsIdwUfVSVHCGEmLRdB+3x8eKTQdZ9tNBPD4/beuW57r/VSbSoek3cv7MZhMXV4ujkiuUo8mZrNl6mM+3/86GXceoXtpF+/rluax6Kaz6u5jvAoEA89bv5oe9x7nzyuo0qablOUTkzKl0KmI6NKjATwcTmfvVbupWiDrnYcXZ7Trgq1wVxyszVTqJiBSUrCwi+txF6KoVpD3+JJkDh/yfvfsOb6puwzj+TdKmSfcutHSwWlbZLUNB9t5DUERBpgNUcICivioiblEQB+AABGQjskERZMreu4PVvdt0JOf9o6VQ2dL2dDyf6+JKclbuhjYn58lvqJ3ogeSYLVxMKNhN7nJCOnm95PByNFDTxzm/BZOfm72qY69Y67S42dvc88xoWTnmG4pSty5QpZiyuZqUTnJGNpk5Fn7dE4bBWkddXxcaBrhR388Ve0P56DZ5jcWisO10FEv2hJGQnkXjym4MaFKZitL1RhQRLycjTzSvSt/G/mw/Hc36I5eYueUUv+y6QLtaFWlTq6KMNVSI1hy6yMajl+lSrxIdgn3UjiOEKGWk6FTKaLUaxnWvy+hvt/HDtrO81LHWf2vyr9OR8fQI7P/3BrojhzEH1y38sEIIUY5pUlNwfPIx9Nv/ImXqp5ieHqF2pPuiKAoxKabcgb6jc1swXYhJJdtsAXIH+q7i6UDTqh5U8cxtxeRQyosteisd7g463B3ubexEU7aZK2lZbDkUyYHwePacj0WrgaCKTjQKcKNhgBuejmV3oHhFUTgcmcDCXReIjE+jmpcDz7evSVBF6XYjiodRb0X7Ot60rV2RI5EJbDhyiaX/hLNyfwRNq3nQMdiHyh4Oascs1XaejWbBrgs0rerBwKaV1Y4jhCiFpOhUClVys6d/aAC/7DzPzrMxNK/u+Z+OY3r8Cew+eh/j7G9J/WJGIacUQojySxMfh9NjfbE6fIjkGd+R2X+g2pHuKi0zO7/10rVucsmmbCC3xVCAuz1ta1WkqlduKyYPB0O5H+fIYK0jtLonlZ2NWBSFC9Ep7A+PY19YHPN2nGfejvNUcrXNLUD5u1HZ0+E/dy0sacJjU1mw8zxHLyXi6WhgTPuahFZxL/e/E0IdWo2Gen6u1PNz5XJiOhuPXmbbqSi2n46mupcjHYN9aFzZTbre3afjlxL5dsspalR0YmTroDLz/iWEKF5SdCqlOgX7sPd8LD9vP0stH2ec/0MTYsXZBVO/gRgWzSftzXdR3NyKIKkQQpQv2qtXcHq0F7oL50n+YT5ZnbqoHekm2WYLEbGp14tM0SlcTcoAQANUdLGlnr9rfjc5X1c7uVi7C61GQ1UvR6p6OdI/tDLRyRnsD8stQP12IJKV+yNxttXTwN+VRgHu1PJxLpXTvselmli8J4y/T0djZ2PFE82r0q52Rfn9ECWGt7MtTz1cjf4hAfx16iobjl5m+qYTuNjpaVfbmzY1K+JgLN2tMovDxfg0vlh/DE8nIy91qlUq36+EECWDFJ1KKa1Ww4jWgbyxeD8//HWGF/9jN7uM4aMw/jwHw/yfSv1YI0IIoTZteBjO/XqgjYkh6ZclZLd4RO1IWBSFK4np+cWl89EphMemkmPJHYnJyVZPNU8HWgZ55XaT83DA1kY+HjwoT0cjnepWolPdSqSasjkYEc/+sDh2no3hjxNXsbHSUtfXNXccKH/XEt81MT0zh98ORrLu8CVAoWv9SnRv4Ied/K6IEsrWxopOdSvRoY4PByPi2XDkEov3hLFiXzjNq3vSoY4P/u72ascskRLSMvl4zVH0Vjpe7VIHO5uS/f4khCjZ5JNCKebtbPvA3ezMNWqS1eIRjD/MIuPZsWAlvxJCCPFf6E6dxKl/TzSmDBKXriKnUYiqeRLSMvlp+1lOXkki1ZQDgI2VlsoeDnQM9snvJudqZyNdooqYvcGahwO9eDjQi2yzheOXEtkfFsf+sDj2XohFo4GgCk40zBsHqoJTyRkHKsdsYcvxKyzfF0GKKZuHqnvSPzTgnse9EkJtWq0m/2/rYnwaG45eZvvpKLaejKJGRSc6BvvQMMANnVbeByG3q/XHa46SlpnDmz3ryd+6EOKBSYWhlCuMbnYZw0fj9NRj6Nf+Tlb3nkWQUgghyjarg/txGtgHxcqaxJXrMNespWqeCzEpfLbuGOmZObQO9sHH0UA1T0d8XGzRyoWVqqx12vyxZ4a0qMaFmFT2h+cWoH7ZeZ5fdp7Hx8WWhgFuNApwo4pK40ApisLeC7Es2nWBqGQTtXyceaxpZRmUWZRqlVzteLpldR4NDWDryatsPHqZaRuO425vQ7s63rSuWaFct+rJMVt4b/F+LiWk83Ln2tISTAhRKO6rc+727dtp167dTcvXrFlDx44dCQkJ4c033yQzM7PA+pMnT/LYY4/RoEEDhgwZwpUrVwqsT0hI4KWXXqJRo0Z07dqV3bt3F1hvsVj44osveOihh2jRogU///zz/cQu0651s8vMMfPDX2dQFOXuO/1LVodOmP38Mc76pggSCiFE2Wa9YztOfbqj2DuQ+Nt61QtOe87F8N7KQ2g1Gt7qVZ+XutWldc2K+LrZScGphNFoNFTxdKBfSABT+jfis8dDGPxQVZxs9fx+MJL/LT/ImLm7mb31NAfC48jKMRdLrtNXk3h3xSG+3HACa52WlzvXYWK3YCk4iTLD3mBN1/q+fPp4KC90rIW7g4GFuy4wdu5u5vx1hovxaWpHLFZpmdkcv5TI15tPcuBCLMNaVifY11XtWEKIMuKeWzqdO3eO8ePHY2dnV2D5jh07GD9+PP3796d9+/bMmDGDKVOm8M477wAQFxfHkCFDqFq1KtOmTWP9+vWMHDmS5cuXY5XXlWvMmDGEh4czZcoUrly5wjPPPMPKlSvx9fUF4KuvvmLWrFm8/PLL+Pj48NZbb+Hu7k6XLiVvcFY1eDvb0i8kgAW7LrDrXAzNqt1nNzudjoyhI7B/ZxK6o0cw1wkumqBCCFHG6Deuw3HYk5j9/ElavBJLRW/VsiiKwsr9ESzZG051L0de7FgLp//Q+lWox9PRSMdgHzoG+5CWmc2hiAT2/WscqGBfFxr6u9HA363QB0O+mpTBot0X2Hs+FmdbPcMeqU7LoArS7UiUWTqthpDK7oRUdic8NpUNRy+x7dRVthy/Qh0fZzoE+1Dfz7XMFOwtikJ0komIuFTC49KIiEslIi6NuNTrDQaGtA6iZZCXiimFEGXNPRWdDh8+zIgRI/D19SU+Pr7Aui+//JKQkBDeffddAAICAujUqRNjxozB3d2dn376CbPZzDfffIODgwMPP/wwHTp0YOPGjXTu3Jnt27ezd+9e5s2bR0hI7vgXp06dYs6cObz99tukpqYye/ZsRo8ezZAhQwBITU1lxowZUnS6Qee6ldh7IZaftp+llrfzfV9omB5/AruP3sc4+1tSP59eRCmFEKLssFm2GIfnR5FTO5ikhctUnQE0K8fM93+eZufZGB6q7smwRwJlpqFSzs7GmubVPWle3ZNss4WTlxPZlzcO1D8X4tBoILCCIw393WgU4E4F5/8+DlRKRjbL94Wz+fgVrLQa+jb2p3O9ShisdYX4EwlRsvm72zOiVRADm1ThjxNX2HjsMp+tO4ano4EOdbxpGVShVE2yYMo2czE+jfDY3MJSRF6RKTPHAoBWk/vFdVAFR/zc7PFzt8PfzZ5q/m7ExKSonF4IUZbc0zvn3r17ee211wCYPv16QSItLY1Dhw7lt2oC8PX1pWrVquzatYtu3bqxc+dOWrZsiYNDbpNsrVZLq1at2LlzJ507d2bnzp14eXnlF5wA2rRpwyeffALAvn37yMzMpFu3bvnrW7duzYQJE4iKisLLSyrxkNvNbmSrIN5Yso8ftp3hhQ73N5ud4uKKqd9ADIsXkPbmOyiu6l08CSFESWf4aQ72r75EdtPmJM9bhOLgqFqWhLRMvlh/nHPRKTwaGkD3Br4yMHgZY63TEuzrSrCvK089XI2w2NT8gcgX7LrAgl0X8HaxzStAuVHV697GgcrKMbPuyCV+OxBJZraZVjUr0qex/38aH1KIssLBaE2Phn50qVeJfy7EseHoJebtOM/iPWG0DKpA+2BvvJ1t1Y6ZT1EU4tMyc4tKsWmE57VeikrK4NqgG7Z6HX5u9jxSowL+7vb4udnh42InX04IIYrFPRWdhg4dilarZdmyZQWWx8bGYrFYCAwMLLC8UqVKhIWFARAVFUX79u1vWr9ly5b89dWrV79p/aVLlzCbzURHR2NjY4O/v3/+emdnZxwcHIiIiJCi0w28XWzpGxLAwl0X2H0uhqb32c0uY9hIjHN/wDDvZzLGvlREKYUQopT76CMcXnuNzHYdSJ49F4zqzTQWFpvKZ2tzZxl6oWMtQiq7q5ZFFA+NRkNlDwcqezjQNySA2BRT7kDkF+JYe/giqw9G4mi0pkFeAapOJWf0VgVbLFkUhb9PR7NkbxhxqZk08HdlYNMq+LiUnAtpIdRmpdPStJoHTat5cCEmhfVHLuW3gKrr60LHYB+CfV2KdaD/bLOFywnpN7Reyr1NzczJ38bT0YC/mz0PVffEz90efzc73OxlllIhhHruqeik1d66Cm4ymQDyWzFdY2trS0JCAgCZmZk3rbezsyuw3tHR8ab9s7OzSU5OxmQy4eDgcNMbpa2t7U1d/QR0qVuJvedj+XH7WWreZzc7c63aZD3cEuOPs8h4dgxYlZ4mxEIIUeQUBbsp78K0TzH16kPK9O9Ar16LkL3nY/lmy0nsbKx4q1d9mWWonHJ3MNChjg8d6viQlpnD4ch49oXFsed8DFtPXkVvpSW4kgsNA9yo7+9K5PlYvll3lPC4NCp72DO6TRA1vZ3V/jGEKNEqezgwuk0NHmtahS3Hr7Dp+BU+XnOUCk5GOgR70yLQC6O+cD83J2dk5XeLu1ZkupyYjtmS235Jb6XF19WOkCru+LnlFpcqudlhW8g5hBDiQT3Qu5I+78O2TlfwGzSNRpNfkLK2tr5pPVwvWOlv8YH9WoEpMzMTvV5/y6LXjc9xr9zcys4Hco87zCAzoU8Dnv1+O7/sCePNfg3v75uNcS9Cnz547PwD+vR58KB57pS3JJK8RUvyFi3JWwTMZnjuOfj2Wxg5EsPXX2O4xbmtOCiKwsK/z/HjH6eo4ePM2482wtXecMd9SsVrfAPJ+994AAGVXOjRrCrZZgtHwuPZefoqu05Hsy8sDg2gAF7ORib0rs8jtb2LtZXGf1VSXt97JXmLlpp5PYBq/m4M7VCLbcevsHzPBX7efo4le8PpWN+XHo398Xa1u3m/O2Q2WxQuxaVyPiqF81HJnI9O5tzVZOJvGNzb3cFAFS8HmtesQFUvR6p4OeLtaldkg/zL70TRkrxFS/KWPA9UdHLLGzQ1OjqagICA/OWJiYn53eHc3NyIjo4usF9iYiLGvO4Irq6unDx58qb1AEajETc3N+Lj4zGbzQWKV0lJSdja3l8z8Li4VCwW5e4blnAeHg53HODPCPRt7M/C3RdYvesCTat53PvBm7bC1dcP82dfkNSi/d23vwd3y1vSSN6iJXmLluQtApmZOD47ApvfVpA+dhy2X3xCTGyqKlGycizM2nqaHWeiaV7Ng+GtgjBnZBOTkX3bfUrFa3wDyVt4fB1s8G3kT/+GfkTEpbE/PA4vN3tCfF2w1mmJU+n3+H6U5Nf3ViRv0SpJeYMrOBLcox5no5JZf+QSq/aGsWL3Ber7u9Ix2IfaPs5oNJoCmdOzcoiMyxt3KTa3FdPFhDSy8gb31mk1+LjYUsvbKXdwb7fcwb1vmqlSUYiPK5q/35L0Gt8LyVu0JG/RKm15b0er1dyxgc8DFZ0cHR3x9vbmwIEDhIaGArnfwB4/fjx/YPAaNWpw4MCBAvsdO3YMT0/P/PXLli0jMzMTGxsbAI4ePYrBYMDR0ZGgoCBycnI4fPgwDRo0AODcuXNkZGTkH0PcrHO967PZ1fRxwsl4j11ArKzIGDIc+/feQnfsKObadYo2qBBClGCa1BQcnxqEftufpL4zhYxnnsdWpZYhielZfLH+GGejUugfEkCPhjJguLg3Go0Gf3d7/N3ty8wHXCFKimpejlTzcuTxtEw2HbvCH8evMDX8CD4utrSqUQGttY4TkfGEx6YRk3K9l4a9wQo/N3va1KqIv9u1wb1tsdLJ4N5CiLLlgd/VOnbsyKJFi0hOTgZg3bp1xMbG0rx58/z1O3fu5MiRIwBcvnyZLVu25K9v3bo1mZmZLFiwAIDs7GwWLVpEs2bN0Gg0+Pr6Urt2bWbNmpX/nPPmzcPJyYnatWs/aPwyS6fVMLJVIBlZOfy07ex97Wt64kkUoxHjnO+KKJ0QQpR8mrg4nPp2x3rHNpK/+oaMZ55XLUt4bCpvLztAZFwaYzvUpGcjPyk4CSFECeJiZ0P/0AC+eKIJI1sHYqXTMn/neeb9dYaL8elU8XSgf2gA4zvX5ssnmjDzqWa83r0uTzSvSosgL/zd7aXgJIQokx54pLkRI0awevVq+vXrR/369Vm7di1t2rShTp3cFjKtW7emadOmPP3007Rp04YdO3bg6urKo48+CuR2r3v22Wf5+OOP2b9/P5GRkZw5c4b33nsv/zleffVVhg0bxuDBgzEajWzdupWJEydiJQNd35GPqx19QwJYtDt3NrsmVe+tm53i4oqp76MYliwibdL/UFxcizipEEKULNqLkTg92gvdxUiSf/yFrI6dVcvyz4VYZm4+ia2NFZN61qNyOej7L4QQpZXeSkvLoAq0CPQiOtlENT9XUpIy1I4lhBCqeeByupubG0uXLqV+/fqcPn2ap556is8//zx/vUaj4ZtvvuHxxx/n9OnThIaGsmDBAuztr/f5e/bZZ5k8eTLR0dE4OTkxd+5c6tatm7++adOmzJs3D71eT0JCApMnT2bIkCEPGr1c6FKvElU8HPhx21mSMrLueb+MYaPQZGRgmD+3CNMJIUTJozt9CuduHdBGRZH06wrVCk6KorDqQATT1h+nkqsd7/ZpIAUnIYQoJTQaDV5ORgwym5wQopzTKIpS+kfWvkflZSDxf7sUn8YbS/bTMMCNsR1q3fN+Tr26oIuMIH7PIXiAWZpK2/gRkrdoSd6iJXkfjNX+f3B6vB/orEhctBxzneCbtimOzNlmC7O3nmb76WiaVvNgZKtA9Fb/7X24pL3GdyN5i5bkLVqSt2iVtrxQ+jJL3qIleYuW5FXH3QYSl47D5YCPqx19QvzZcz6W3edi7nm/jOGj0UVGoF+/tgjTCSFEyWD95xac+3RHcXAkYfWGWxacikNSRhZTVh1m++lo+ob481zbGv+54CSEEEIIIYSapOhUTnSt50tlD3t+2naW5HvsZpfVqQtmn0oYZ31TxOmEEEJdNiuX4TSoP+aAyiSu3oClchVVckTEpfLW0gOEx6Uytn1NejfylwHDhRBCCCFEqSVFp3JCp9UwqnUQ6Vk5/LT93L3tZGVFxtAR6Lf/he7E8aINKIQQKjH8MAuHkUPJadiYxJVrsHhVUCXHvrA43ll+EItF4c2e9Qi9x8kfhBBCCCGEKKmk6FSOVHK1o09jf3afi2HPPXazMz3xJIrBgHHWt0WcTgghipmiYPvphzi8No6s9h1JXLQcxclZhRgKqw9E8sW6Y3i72PJuXxkwXAghhBBClA1SdCpnutbP7Wb34z12s1Nc3TD1fRTDkoVoEhOKIaEQQhQDiwW7N17F7sP3MT36GMk/zAdb22KPkW228N0fp1m4+wKhVT2Y1KMeLnY2xZ5DCCGEEEKIoiBFp3JGp9UwMq+b3c/32M0uY9goNBkZGObPLeJ0QghRDLKzcXh2BLazviV91HOkfDkTrK2LPUZyRhYf/HaYbaej6NPYn+fb1cDGWgYMF0IIIYQQZYcUncohX1c7ejf2Z9e5GPacv3s3O3OdYLKaPYTxh+/BbC6GhEIIUUTS0nB8ciCGZYtJfeNt0t6dAtriPxVGxqfx9rIDXIhJ5fl2NenTWAYMF0IIIYQQZY8Uncqpbte62f11lpSM7LtunzF8FLqIcPQb1hVDOiGEKHyahHic+/dE/8dmUj79kowXxoMKhZ4D4bkDhmebFSb1rEfTajJguBBCCCGEKJus1A4g1KHTahjZKohJS/fz099neb5dzTtun9W5G2ZvH4yzviWrc9diSimEEIVDe+UyTgN6ozt/juTvfyKre89iz6AoCmsPX2LBzvP4u9szrlNtXO1l/CYhhBBClCwx6TFsjthA3LGrpKaZUFBAUbAoCgoKiqJgwYLyr8fkPbYolvzluY+VG7a13P4Yeeu4dgwF8tbmPb75GDc+X6NKDXi+zssYrYxqv4TiBlJ0Ksd83ezo08ifxXvDaFLFg5Aq7rff2MqKjKHDsX//HXQnT2CucecilRBClBS682dx6t8LTXw8SQuWkt3ikWLPkGO2MOevM/x1KorQKu6Mah0k4zcJIYQQokRQFIUT8cfZELaW9WFr2R/1T17x5zqtRosGDRqNBg2afz3WFlyu0aD51z78ax/tv/Yhb5+Cx7j982g0N6wDFODjHR+z/sxG5nSci5+jf/G/kOKWpOhUznWtX4m9F2L5YdsZalR0wsF4+8F0TU8Mwe6TqRhnf0fqx58XY0ohhPhvrI4cwmlAb1AUkpavJqd+w2LPkJyRxbQNxzl1JZnejfzo3dgfrYzfJIQQQggVZZoz+fvSNjaErWVj+HoiUyIAqO/RgFdCJtIxoDOtajYnLjZN5aT3bnfCVgYtfYL2i1sys/0s2vi1VzuSQMZ0KvesdFpGtg4kLTOHn/8+e8dtFTc3TH36Y1i8AE1iQjElFEKI/8b672049eyCYjCS+NsGVQpOF+PTeHvZQc5Hp/Bcuxr0DQmQgpMQQgghVBGbEcvCk/MZuu4JasypzMDVfVhwch613GrzaasvOfzUKTb038rLIRMI9qiX2wKpFOkW2I2N/bfibV+Jx1b349N/PsSiWNSOVe5JSyeBn5s9vRr5sXRvOKFVPQipfPtudqbhozAumIdhwXwynnm+GFMKIcS9069ZjeOooZgDKpO0aDkWb59iz3AwPJ7pm05gY61jUo96VPVyLPYMQgghhCi/FEXhZPyJ/G5z+6L2oqBQwa4ifas/SseATjxc6ZEyNQZSZacq/N5nI69sfZEP97zP/qh/mNH2O5wNLmpHK7ek6CQA6F7fl38uxPHDX3nd7Ay37maXE1yP7CbNMM7+joyRz4BOxiQRQpQshl/mYj9uDDkNGpI0fzGKq1uxPr+iKKw7fIlfdp3Hzy13wHA3GTBcCCGEEMUgy5zFjsvb2RC2lg1h64hICQegnkcDXg6ZQMeAzgS718sbZ6lssrW2ZXrbb2lcIZRJ21+j/ZJHmNNpHsHuddWOVi5J0UkAud3sRrUO5M2lB5i7/SzP3mE2u/QRo3Ea/hT6TRvI6ti5GFMKIcSdGb/6Avv33iKrVRuS5swDe/tiff4cs4Uftp1l68mrhFR2Z1SbIAwyYLgQQgghilBcRhybwtezIXwdf0RsJjU7BYPOQMtKrRjbcBwdAjpRwa6i2jGLlUajYWid4QS712XY+ifpurQdHz/yBQNqPK52tHJHik4in5+bPb0a+rH0n9xudo1v080uq3M3zBW9MX7/jRSdhBAlg6Jg986b2H79JaZefUiZ/h3o9cUaISUjm2kbjnPyShI9G/rK+E1CCCGEKBKKonA64RTrw9ayIWwt/0TtwaJY8LKtQO/qfekQ0JkWPo9ga22rdlTVNa4Qyqb+2xi1cShjtoxmX9Re3nt4KjY6aYVeXKToJAro3sCXfy7E8sNfZwi6XTc7a2tMQ4djN+VddKdOYg6qUfxBhRDimpwcHMaNwbBwPhlDh5M65eNi7/p7KT6NT9cdIyEtk2fb1qB5dc9ifX4hhBBClG1Z5ix2Xv47t9tc+DrCk8MAqOtRn3GNXs3tNlcKB/8uDh62HvzafQVTdr/L9ANfcDjmILM7zsXHoZLa0coF+Y0UBeTOZhdEamYOc/8+d9vtMp4YgmJjg3H2t8WYTggh/iUjA8enn8CwcD5pL08gdeqnxV5wOhQRz/9WHCQz28wbPepJwUkIIYQQhSLeFMevpxYwfP1T1PyhCv1/68nc4z8S6BLEx498waEnT7Kp/1+8Gvo69TwbSMHpDqy0VrzV7F1md5zL6YTTtFvcgm0Xt6odq1yQlk7iJv7u9vRs6Meyf8JpUsWdRrfoZqe4u5PZux+GXxeS9sbbKE7OxR9UCFGuaZKTcBw8EOtdO0j54GNMw0YV6/MrisL6I5eZv/Mcvq52jOtUG3cHQ7FmEEIIIUTZoSgKZxJOsz48t9vc3qu7sSgWPG296Fm1Nx0COtOyUivpNvcAulftSU3XWgxdN4j+v/Xkjab/4/n6L5TpgdXVJkUncUs9Gviy70Isc/K62dnfoptdxvBRGBbOx7BgHhmjn1chpRCivNJER+M0sA9WJ4+TMnMWmX36F+vz55gt/LT9LH+cuEqjADeeaVtDBgwXQgghxH3LNmez68oONoStZX3YWsKSLwAQ7F6Plxq9Qgf/TtKKqZBVc6nO2n5bGPfH87y38y32Xd3LV21n4qB3VDtamSRFJ3FL17rZvbXsAHP/PsczbW8etymnbn2yQ5tinP0dGSOeKfYuLUKI8kkbHoZz/55oo6NImreI7Dbti/X5k9Oz+PD3I5y4nESPBr70C5UBw4UQQghx7xJM8WyO2MiGsLVsidhMclYSNjobWvg8wrP1x9IhoBPe9j5qxyzT7K3t+bb9DzTyCuF/OybRYUkrfug0nxqut5/FXfw3UnQSt+Xvbk+PBr4s3xdBaFUPGgW43bRNxvBROI4cin7zBrI6yEx2QoiipTt+DKcBvdFkmkhcvJKckCbF+vyXE9L5fNE/xCRlMLpNEA8HehXr8wshhBCidApLusBPZzaw7NgKdl/ZiUWx4GH0pHvVnvnd5uys7dSOWa5oNBpG1XuOeh4NGL7hKTotac3nrafTu3o/taOVKVJ0EnfUs6Ef+8LicrvZVXC8qZtdZtcemCtUxDjrWyk6CSGKlNWunTgNHoBiNJK4aj3mGsX3TZRFUdh64iq/7DqPjbWON3rUo3oFaYIthBBCiDs7GnuEL/d/yqpzK7AoFmq7BfNiw/F0COhMfc+G0m2uBGjq3ZzN/bcxfMNTjNr4NPuj/uGtZu9hrbvFTO7ivknRSdyRlU7LqLxudvN2nGN0m391s7O2xjRkGHZTJ6M7cxpz9UB1ggohyjT9xnU4Dn8Kc0Vvkn5dgcXPv9ieOyIulR/+OsuZqGRqVHTi9X4N0Wabi+35hSivciw5pGenqx1DCCH+kz1XdjNt/ydsDF+PvbUDz9d/kfGPvIAxy0XtaOIWvOwqsKzHat7d+SbfHv6agzEHmNXhJ7zsKqgdrdSTopO4qwLd7Kp40PBf3ewyBg/F9rOPMM7+Nne6ciGEKEQ2ixfiMPYZcmoHk7RgKYqHR7E8rynbzLJ/wll3+CJ2NtaMbB1Ii0AvPJ1tiYlJKZYMQpQnGTkZ7I/6h11XdrDr8g7+idpLWnYqlex9qe4SSKBLENVdgvJv3Yw3d/sXQgg1KYrC1ot/8MW+T9hxeTuuBlcmhr7J08EjcLJxxsPJQT5DlGDWOmvee3gqDb0a89Ifz9N2cQtmdfiJpt7N1Y5WqknRSdyTa93sZv91hsB/dbNTPDzI7NUXw8JfSHv9LRRHJxWTCiHKEuN3X2M/aQJZD7ck+adfUByKp0vbvgux/Pz3OeJSM2ldswIDmlS+5SyeQoj/LikzkT1XdrHryk52XdnBwej9ZFuy0aChplttBgQ9RoC7LwcvHeFMwml2XdlBRk5G/v5uBjeq5xeiAvMLUj72lWTqayFEsbIoFtZe+J1p+z7hYMwBKtp5895DH/BErSEyTlMp1Lt6P2q61WboukH0XtmV/zWfzMi6z8q55T+SopO4J/mz2S3df8tudhkjRmP4dQGGhfPJGPmsSimFEGWGomA79T3sPv+EzK49SJ45CwyGIn/a2BQTP20/y4HweHxd7XiuVw0CK0ghXYjCEJV2NbcV05Ud7Lq8k+NxR1FQsNZaU8+jAaPqPUfTis0IrdAUZ0Nu9xMPj+utAiyKhYspkZxJOMXphNOcTTzN6YRT/H5+JXNN8fnPY2tlR3WXwJtaRwU4VpbxOYQQhSrbnM3ys0v4cv9nnE44RYBjZT5r9RX9gwZio7NRO554ADVca7K+7x+M2fIMb/49kX1Re/ms9XTsre3VjlbqSNFJ3LMAd3t6NPRjxS262eXUa0B241AMs78jY/ho0MqAeEKI/8hsxv618Rh/nkPGE0+R+vEXoNMV6VPmmC2sO3yJ5fvCAXisaWU6BvtgpZP3MiH+C0VRuJB8nt2Xd7Lzyt/suryDsOQLQG5RqHGFUF4JmUgz74do4NkIW2vbux5Tq9Hi5+iPn6M/bf07FFgXmxGbV4w6lX+78/LfLDm9KH8bK60VVZyq3tQyqqpzdWmJIIS4L6YcEwtPzmf6gS+ISAmnpmstvmk/mx5Ve2OllUvsssLRxokfO83nqwNfMGX3O5yIO84PneZTzaW62tFKFfmLEPelV0M/9l3Im82uoiN2Nte/McwYMRrHUU+j37KRrHYdVUwphCi1MjNxfHYENr+tIH3sONLeeBuKuCnzqStJ/LDtDBfj02kU4Mbgh6ri7lD0raqEKEvMFjPH44+x+/KO/O5y0elRALgaXAmt2IyhdUbQtGIz6rjXLfQWR+5Gd9yN7jTzfqjA8tSsFM4mnskrRuW2jDoVf4J1F37HrFyfEMDXwS+vdVTQDa2jAnE1yLhRQojrUrNT+enoHGYe+oro9CgaeTXm/RYf0d6/o8xCV0ZpNBrGNnyJ+p4NGLVhKB2WtOKrtt/QtUp3taOVGlJ0Evclt5tdIG8vO8C8v88zqk1Q/rrMbj0xe1XA+P03UnQSQtw3TWoKjk8NQr/tT1L/9z4Zz44p0udLMWWzcNcFtp68ipu9DS91qk2jALnAVEtSZiLzT8zFYpWJjcUOF4MrLjYuuBhccTa44GrjiqONk3yoLyEyzZkcjD7A7rxBv/dc3U1yVhIAPvaVaOHzCE29m9O0YnOquwSq9v9mr3egvmdD6ns2LLA8y5zFhaTzBVpGnUk4zc7LfxcYN8rd6J47bpRzwdZR3vY+JWZsD0VRyLZkY8rJwGTOxJSTQaY5E5PZdP1+TgamnExM5gwyc66tM2Fnp8dR40Ylez98HXzxtPVCpy3alqVClEYJpnhmHfmW7w/PJDEzkRaVWjGz3Swe9mlZYt4LRNFqWakVm/pvY9j6wQxdN4gxDV5iYpM3pWXbPZBXSNy3yh4OdG/gx8r9EYRWdaeBf95FmrU1piHDsPvwfXRnz2CuJs0OhRD3RhMXh9NjfbA6cpjkL2eSOXBQkT2Xoij8dSqKBbvOk5Flplv9SvRq5I/BWi601JCSlcx3h2fyzaEZJGUm3nFbrUaLs41zbiHKxgXXG25vLE45Gwqus7O2l4uCB5SalcLeq3vYfWUHO6/s4EDUPkxmEwCBLkH0rNaHphWb0dS7Ob4OfiqnvTu9Tk+Qaw2CXAuOUfnvcaOuFaR+O7echMyE/O1uN26U0SmIBFM8meZMMm4s+NxQDMrMMd1+ndlERo6JzLyi0I2PM3NM+cUi078eKyiF8rpYa62paO+Dr70vPg6VqOTgi6+9H5UcfKnkUAkfe18MVtISVJQfUWlXmXloOj8enU16ThqdKnflhYbjaOQVonY0oQIfh0qs7L2ON7dP4KsDn3Mgeh/ftv8BD9vimVm5tJKik/hPejXyY19YLHO2nmHqgOvd7DIGD8X2848xzv6W1A8+UTmlEKI00F6MxOnRXuguRpL84y9kdexcZM91MT6NH7ad4dSVZAIrODK0RXV83WQsFzWkZqUw68i3fH3wSxIzE+kU0IVXQibySI1mnLkYSWJmPPGmeBJM8SSYEkjIjCfRlJC3LIGEzASupl3lZPwJ4k3xpGWn3va5rLXW14tUBpcCraiuFadcDK64GFxwscm7NbhitDIW4ytSssRmxLLr8o7clkxXdnI09jBmxYxOoyPYvS5P1RlG04rNaVKxGe5Gd7XjFprbjRulKMotx43acWl7gXGjHoReq8dgZcRGZ4PByoBBZ8Am79ZgZcDB6FDgce52Rgx5tzZ5y3P3s8GgM2LIu735sQGDzgY3d3sOhZ3kUkokkSmRXEyJ5GJq7u32i39xNf0KFsVSIKeH0ZNKDpWo5OBHJXtffB188XHwzStQ+eJk4yxFXlHqhSeHMf3ANBaenEe2JZte1foytuE4arnVVjuaUJmNzoaPHvmchl6NeXXrS7Rb3ILZHX+mcYVQtaOVWFJ0Ev+JtU7LqNZBud3sdpxnVOvcbnaKpyeZPftgs/AX0l5/q9imNxdClFInTuDcrQOalBSSfl1BdtPmRfI0mdlmVuyPYM2hixitdQx/pDota1RAKxdGxS41O5U5R77j64NfEm+Kp4N/J14JmUg9zwYA6LQ63IxuuBndqHofx80yZ5GQmUCC6XpxKjEzIb9wde1+oimB8KQwDmUeIMEUn99a51aMVsbrBambClO5931iPMlJ12C0ssXW2hajlS1GKyO21nYYrYwYrYwlvkugoihEpkSw68oOdl/Zya7LOziTeBoAg85AQ6/GvNBwHE29H6KxVwj2egeVExc/jUaDh60HHrYeNPd5uMC61KyU/PGi0jSJ5JgoUBy6XkjKLRDdWBzKLR7l3qrxe+Jo40AN15rUcK15y/XZ5myupF3mYkokkSkRXEq9mH//eNxRNoatu+lvyN7aIbcoZe+b10Iq75904ROlwKn4k3y5/zOWnVmMTqNjQI1BPN/gBSo7VVE7mihhBtYYRG33YIaue4KeKzoz+eEPGVJ7mBTdb0GKTuI/y+1m58vK/ZGEVrnezS5j+CgMixdiWDifjBHPqJxSCFFSWR0+CAN6o9HqSFy5FnOd4CJ5ngPhcfy8/SwxKZm0DPLisaZVcDDKtOnFLS07jR+OzmLGgS+IM8XR1q89r4RMpKFX40I5vl6nx8vWCy9br/vaLz07/XpBKq9olWDKvS2wLDOB0wkn85flWHLu+TmMVkZsrWwLFKZyb425929YZ3tDwcrWyg6jdd7ttW1vsc39DsptUSycTjjFrss72JU3JtPltEsAOOqdCK3QhAE1BtG0YnPqedaXab/vwl7vQAOvRjTwaoSHhwMxMSlqRyo01jrr/JZft3KtBdjFlAgu5hWkLqZEEJkayaWUi+yL2lugWyJIFz5RMh2M3s8X+z5lzYXfsLWyZUTdZ3im3vNUtPdWO5oowYLd67Kp31ae2zyS1/4ax76ovXzU8vN7mpG1PJGik3ggvRr5sy8sLq+bnRN2NlbkNGhEdqMQDLO/I2PYKLUjCiFKIN3xYzj17wkODiQsXoWlcuF/gxiXmsm8v8+x90IsPi62TOpRlxrezoX+POLO0rPT+enYHL468DmxGTG08m3DKyETCanQRO1oALlFHGtbvO197nkfRVFIzU4h3hSPjT1cjI4mIyeD9Jx0MrLTc29z0knLzr1Nz7v99zYJpnguZV/MW55GenYGGTnp9z0+j5XWKr8w9e/WVrb/KnhFZ11hW9i2/EKAl20FmlZsTlPvZjSp2JyarrWkFYq4Zze2AGvg1eiW26Rmp3Ip5WJuMeoBu/A1pi4e+BbHjybKAUVR2Hn5b77Y/wl/Rm7BycaZcY1fZUTwM7gZZWIRcW+cDS7M7bKIz/d9zEd7pnA09gg/dJonreNuIEUn8UCsdVpGtgrif8sPMH/HOUbmdbPLGD4Kx2eGo/9jEwzsq3JKIURJojt9Cud+3VEMRtiyBYujZ6Ee32xR2HDkEkv/CceiKDzaJIAudSthpSvZXZzKGlOOiZ+PzeHLA58TnR5Fi0qteDXkdZpUbKp2tAem0Whw0DvioHfEw8OBCtrCa9miKAoms+mGYlUG6dlpNxWmrhW2brlNTkZ+oSvBFH/DunQ87NzpVLlr7nhM3s2o7FhFugKIImVvbX/LQduvuVMXvhNxx27qwvewT0teC51UJt5LhDoURWFT+Hq+2P8pe6/uxt3owaSm7zC0zjAc9DI0iLh/Wo2W8Y1fo4FnQ0ZvHEb7xY/wdbvv6BBQdOOUliZSdBIPrIqnA93q+7LqQCShVTyo7+9KZvdemN9+A+P330jRSQiRT3f+LE59u4NGS9LS33CtWhUKsSvK2ahk5vx1hoi4NOr5ufLUw1XxdCy/g0GrwZRjYv6Jn5i2/zOupl3hIe8WfN/hR5p5P6R2tFJBo9HkjwXlaij8b9rLWvcvUfrdTxe+oyn7mbrtQ7ov70Br37ZMCJ102xZWQvyb2WJm9fmVfLHvU47FHaGSvS8ftPiEx2sOLtcTR4jC08avPRv7/8Ww9U/yxJoBjGv8Kq80nljuWxBL0UkUit6N/dkfFsfsv04z9dHG2NnoMT31NHYffwCnT4NLRbUjCiFUpg0Pw6lPdzQ52SQuX4O5WvVCO3ZaZjaLdofxx/ErONvpeaFDLRpXdpMWHMUo05zJLyfmMm3fp1xOu0TTis35ut33POzTUu1oQohS7MYufB3qtKKP/+P8cHQW0w98TselrekY0JlXQ98g2L2u2lFFCZVlzmLJ6UV8uf8zziedo5pzdb5sM5O+1R+97zHxhLgbf8cAVvfewIS/xvPZPx9xIGofM9vPKpIvkkoL6WsgCoW1TsvI1kEkpWfxy85zAGQ8+TSKXg/DhqFJSlQ3oBBCVdqLkTj36YYmPY3Exasw17j1TEn3S1EUtp+O4pWF//DniSt0quvDRwMaE1LFXQpOxSTLnMXPx36g2fyGvPbXOHwcKrGkxypW9lorBSchRKGzs7bj+QYv8M8TR5gQOoldV3bS9teHGbb+SU7Fn1Q7nihB0rPT+f7wTELn1ePFP57Dztqe2R1/ZtvAPQysMUgKTqLIGKwMfNFmBp+2+pK/L22j/eJHOBR9QO1YqpGikyg0VTwd6Frfl60nozgUEY/i5UXK9G9h926ce3RGe/WK2hGFECrQXr2SW3BKTCRp8cpCm6XucmI6H/x2mG+2nMLDwcB7fRsyqHlVjHppxFscss3ZzD/+M81/acTLW1/Ay64Ci7otZ3XvDbSs1EqKfkKIImWvd2Bc41f554nDjGv8KlsiNtFyYRNGbxzGucQzascTKkrOTGLavk9pPK8Ob2x/DV9HPxZ2W8qm/n/RvWqvct/VSRSfwbWG8Fvv9VgUC92Wd+CXE3PVjqQKKTqJQtWnsT8+LrbM2nqa9MwcMnv1hTVr0EaE49ylHbozp9WOKIQoRproaJz6dkcTE0PSomXk1GvwwMfMyjGzZG8Yr/+6j7DYNIa2rMbbvevj725fCInF3eRYclh4cj7NFzTipT+fx83oxoKuS1jTZxOt/dpKsUkIUaycbJyZEDqJf544wnMNXmDthdU8vCCUF7Y8S3hymNrxRDGKzYhlyq53aTC3Nu/vfoe6HvVZ1Wsdv/VeTxu/9nJ+Eqpo4NWITf230bRic1784znG/zkWU47p7juWIVJ0EoXqWje7xPQs5u88n7uwXTuSVvyOxmTCuXsHrPbtVTekEKJYaOLicO7fA92liyQvWEJO49AHPubhyHgm/LqPFfsiaFrNg48HNqZtLW+08kGyyOVYcvj11AIeWtCYsVuewcnGmXldFrGu7x+09e8gH+aFEKpyM7rxVrN32fvEEYYHj2LZmcU0+6UhL//5IpdSLqodTxShSykXmbT9NRrNrc20/Z/SyrcNm/r/xcJuy2jq3VzteELgZnRjYbdlvNToZeYe/5HuyzsSmRKhdqxiI0UnUeiq5s1mt/XkVQ5FxAOQU68BCas3oDg44ty3O/rNG1ROKYQoSpqEeJz690R34TxJcxeR3fTBPvQlpGUyfeMJPvr9KDqthondgxndpgZOtvpCSixux2wxs/T0r7RYGMrzm0dhZ23PT50XsLHfVjoEdJZikxCiRPG09eS9h6eyZ9Ahnqj5FAtOzqXJ/Pq8vu0VotKuqh1PFKJziWcYvmo4ofPrMfvId/So2pvtj+1ldsefqetRX+14QhSg0+qY2OQtfu68kPNJ52j3aws2ntuodqxiIUUnUSR6N8rtZjd762nSTNkAWKpUJeH3TeRUrY7jEwOwWfSLyimFEEVBk5yE04DeWJ0+SdKPv5Dd4pH/fCyLRWHD0Uu8uugf9oXF0i/Enyn9G1Hbx6UQE4tbsSgWVpxZyiOLmvLMpuHotTb80Gk+m/r/RefKXaXYJIQo0Srae/PRI5+z8/H99A8ayA9HZxE6vx7/2zGJ2IxYteOJ/ygtO42FJ+fTa0UXmv3SiHmH5zG41hB2DzrIV22/obpLoNoRhbijTpW7sLHfn1Sw8+al9S+pHadYyGirokjorbSMbBXI/1Yc5P2l+3mmdRAGax2KpydJK37HccgTOI4ZTWp0NBnPvwBy8SJEmaBJTcFpYF+sjh4h+Yf5ZLdp95+PdT46hR+2neFCTCrBlVx4qkU1KjgZCzGtuBWLYmH1uZV88s9UTsafoIZrTWZ3/JmuVXqg1ch3VUKI0sXP0Z/PW09nTMOX+HTvh8w8+BU/Hp3NyLrP8Gz9MTgb5EuMkk5RFPZe3cOCk3NZcXYZadmpBDhWZmLom4xt8Sy6DDu1IwpxX6o4V2Nj/63YOCooaWqnKXpSdBJFpqqXI8MfCWT21tN8mJbFy11qY2djjeLgSNIvi3F4fhT2772FNvoqae9MAa1czAhRqqWn4zjoUawO7CP5+5/I6tj5vx0mM4fFe8PYdPQyTrZ6nm9XkyZV3aVlTRGzKBbWnF/Nx3s/4ET8MQJdgviu/Q/0qNZbik1CiFKvilNVZrT7jhcajufjvR/wxf5PmHP0e0bXe45R9Z7FQe+odkTxL1fTrvDrqYUsPDmPs4lnsLWyo0e1XjxeYzBNKjZDo9HgYe9ATEaK2lGFuG96nR53Wwdi0sr+768UnUSReqRGBbzc7Plg+QHeX3WYV7sG42yrBxsbUr6dg8XTE9tvv0YbE03Kl9+AXsZnEaJUMplwevIxrHfvJGXmLLK69bjvQyiKwu5zMczdcZ7kjCza1/GmX0gAtjZyqipKiqKwLmwNH+/9gKOxh6nqXI2Z7WbRq1pfmVZaCFHmBLoG8X3HH3kx9mU+2juFj/ZO4fvDM3muwQsMCx6FnbW0mlFTljmL9WFrWXBiLlsiN2FRLDSp2IznG7xIj6q9sNc7qB1RCHGf5JO8KHItalUk25TNF+uP8d6Kg0zoVhcPRwNotaRN/hCLVwXsJ/8PbVwcyT/MQ7GXk4kQpUpmJo5DB2G97U9Spn1NZu9+932Iq0kZ/LjtDEcvJlLZw56XO9emsoe8FxQlRVHYGL6Oj/dO5VDMASo7VWF622/pU70/Vlr5eCCEKNtqu9fhp86/cDB6Px/ueZ/Ju/7HN4emM6bBOIbUGYbRSrpzF6ejsUdYeHIeS04vIt4UTwW7ioxp8BIDazxOVefqascTQjwA+VQpikWwrwsTutXl4zVHeXfFQSZ0C8bH1Q40GjLGjsPi4YnDuDE49e5G0i9LUDw81I4shLgX2dk4jhiCzeaNpHz6JZkDB93X7lk5Zpb/E86qAxFY6bQ89XA12taqiFYrXemKiqIobInYyEd7p3Agej/+jgF82WYm/QIHSLFJCFHu1PdsyIJuS9l7dTdT97zP2zte5+uDX/Jio5d5otZT2Ohs1I5YZiWY4ll2ZjELTs7ncMxB9Fo9nSp35bEag2jl21Za2wpRRsinS1Fsqldw5M2e9Zj6+xHeW3mIV7oGU9UztyVD5mNPoLi54ThiCM7d2pO0aDmWgMoqJxZC3FFODo6jh2Gz7ndSPvgE0+Ah97X7mavJzP51Hxfj02hazYNBzargYicf7ouKoij8GbmFj/a+z76of/Bz8OeL1jPoHzgQa5212vGEEEJVIRWasLTHKv6+tI2peyYzcdvLzDgwjXGNX2VA0OPyPllIzBYzWy9uYcGJ+ay9sJosSxbB7vWY8vBH9Ansj6vBTe2IQohCJiODimLl62bHWz3rYdRb8cFvhzl+KTF/XVaHziQuWYU2MQGXru2xOnJIvaBCiDszm3F4fhQ2v60g9Z0pmIaNvOddc8wWluwN492VB8m2WHitax2eb1dTCk5FRFEU/rr4J92Wd2DA6t5EpUXxaasv2fH4Ph6vOVgupIQQ4gYP+bRgVa91LOq2HE9bT8b9OYbmCxqx6OQvmC1mteOVWueTzjFl17s0mluHgav78tfFP3iq9tNsfnQ7mx/dxvC6o6XgJEQZJUUnUey8nIy81ase7vY2fLzmCPsuxOavywlpQuJvG1D0epx6dsF621YVkwohbsliwX7cGAzLFpP6xttkPPP8Pe96JTGdd1ccYsW+CFoEejFzZAuCfV2LMGz5tuPSdh758RH6rerBpZSLfNTyc3YNOsDgWkPQ62TiBiGEuBWNRkNrv7as7buFeV0W4aB3ZMyW0bRYGMryM0uwKBa1I5YKqdmpLDw5nx7LO9F0fgO+PPAZNd1qMbvjzxwecpr3W3xEsHtdtWMKIYqYFJ2EKlzsbHijZz383OyZtuE4205F5a8zBwaR+PtGLJUq4fRYX2xWLlMxqRCiAEXB/rXxGBfMI+3lCWS8MP4ed1PYfOwyk5bsJzo5gxc61GJk6yDsbKSVTVFIykxk7JZn6LWyC+cSzvFBi0/Y/cRBhtQZJsUmIYS4RxqNhg4BndnU/y9md5yLldaKURufpvWih/j9/G8oiqJ2xBJHURR2XdnJC1uepc4P1Rm75Rmi06OY1PR/HBh8nAXdltK9ai8ZK0uIckTGdBKqcTBYM7F7XT5fd4xv/zhFelYOHYN9ALB4+5C4ah1OTwzAYeRQNDHRmIaPVjmxEOWcomA36TWMP80mfew40l+ZeE+7JaVn8f2fpzkYEU+wrwsjWwVKV7oitCl8PeP+HEtMejQvNnyZKZ3eJTUxR+1YQghRamk1WrpX7UmXyt1YeW4ZH+/9gKHrBlHXoz6vhbxOO/+OaDTlewKMK6mX+fXUAhacnMf5pHPYWdvTq1ofHqs5mNAKTcr96yNEeSZFJ6Eqg7WOl7vUYcamE8z9+xxpmTn0buSHRqNBcXYhcfFKHEcNxeH1V9FGR5M+8U2Qk5YQxU9RsHv3LWy//4b0Uc+S9sbb9/S3uC8sjtl/niYj28yTD1WlfR1v+eBZRJIyE3nz74ksPDmfmq61+LnzAup7NsRobSSVFLXjCSFEqafT6uhTvT89qvZmyelFfPLPhwxa8yiNvEJ4LfQNHqnUulyd4zLNmay/sIYFJ+fxR+RmLIqFZt4P8WKjl+lWtSf21vZqRxRClABSdBKqs9ZpGdO+FrO2nmbZP+GkmrJ54qGqaDUaMBpJnjMP+9fGYffFJ2ijo0j9ZBpYya+uEMXJ9sPJ2M6YRsbQ4aS9+8FdC06mbDPzd5zjjxNX8Xez4422NfBxtSumtOXPja2bXmr0MuMavyZdF4QQoohYaa0YWGMQfas/yoKT8/jsn4949LdeNPN+iAmhk2jm/ZDaEYvUkdjDLDgxl6WnfyUhMwFvOx9eaDiOATUGUcWpqtrxhBAljFy5ixJBp9UwolUgdnor1h25RHpWDiNaBaHTasDKitRPpmHx8MTus4/QxsWS/O0PYGurdmwhygXbzz7C7rOPyXjiKVI/+OSuBaezUcnM3HyS6GQT3er70i/EHyudDCFYFBJNCbz590QWnfqlQOsmIYQQRc9aZ82TtYfyaNBjzDv+I1/s/5SeKzrTslJrJoS+QeMKoWpHLDTxpjiWnv6VBSfnczT2MHqtni5VujGwxhM8Uqk1Oq1O7YhCiBKqUIpOq1evZvz4goPJenl58ddffwGwZs0apk2bRnx8PJ06dWLSpEnY2Fz/BvbkyZO88847nDx5knr16vHBBx9QsWLF/PUJCQm8++67/PXXX1SoUIG33nqLJk2aFEZ0UYJoNRoGNa+CvcGKJXvDSc8y83y7muittKDRkD5hEhZPL+wnvoxz/54kzVuE4iKzXglRlIzTp2E3dTKm/gNzWxlqb188MlsUVu6PYMW+8NzJAnrUpYa3c/GFLWc2hq1j/NYXpHWTEEKozGBlYHjd0Txe80l+PDabr/Z/Rpdl7Wjt25YQ30Zocqyxt7bH3toBO2s77PXX79vl3be3tsdoZSxR3fPMFjN/Rm7ml5PzWH9hDVmWLOp5NOCDFp/Qp3o/XAzyOVwIcXeFUnQ6duwYLVq04IUXXshfZm2dOyPRjh07GD9+PP3796d9+/bMmDGDKVOm8M477wAQFxfHkCFDqFq1KtOmTWP9+vWMHDmS5cuXY5XXhWrMmDGEh4czZcoUrly5wjPPPMPKlSvx9fUtjPiiBNFoNPRq5I+tjRU/bz/HJ2uO8FKn2hj1ub8LpqdHYPHwwPGZ4Th370jSouVYfCqpnFqIssn4/Uzs330TU68+pEz7+o4Fp6tJGczcfJJz0Sk8HOjJkw9Vw9ZGGtMWhX+3bprbeSH1PBuoHUsIIco9W2tbnq0/hidrD2X24W/58dhsdl75G1OO6Z7212q02FnbY29tn1ucsrbHXp9XnMorWtnrr6278f6tC1q21nZoNfff0vhc4hkWnvyFRad+4WraFdwMbgytM5yBNZ6gtnud+z6eEKJ8K5QrghMnThAaGkpwcPBN67788ktCQkJ49913AQgICKBTp06MGTMGd3d3fvrpJ8xmM9988w0ODg48/PDDdOjQgY0bN9K5c2e2b9/O3r17mTdvHiEhIQCcOnWKOXPm8PbbbxdGfFECdajjg53eim//OMWU3w7zapdgHIy5hcys7r1IcnHF8anHce7anqSFyzDXqKlyYiHKFsNPc7B/4zUyu3QnZcb3tx1HTVEU/jx5lXl/n0On1fJ8uxo0reZZzGnLjxtbN41r9AovNX5VWjcJIUQJY29tzwuNxvNCo/F4eDhw+Wo8admppGWnkZqdSmp2CqlZ1x6nkJq3Li0r9/6/111MuUhadmredqlk5GTcUw4NGmzzilK5BSmHmwpatjcUrbQaLRsvrmF7xHa0Gi1t/doz5eGP6RDQCb1OX8SvmhCirCqUotPx48d5+umnb1qelpbGoUOH8ls1Afj6+lK1alV27dpFt27d2LlzJy1btsTBwQEArVZLq1at2LlzJ507d2bnzp14eXnlF5wA2rRpwyeffFIY0UUJ9lCgF0a9FV9tPMF7qw7xWtdg3OxzL66yH25J4sq1OA3sg3OPjiTNW0xOqHS5FKIw2CyYh8MrL5LZviPJ3/0AeS1X/y05I4vZW8+wLyyO2j7OjGwdlP83KgpXoimBSX9P4NdTC6R1kxBClDLWOmucdS44G1wK5Xhmizm/CJVboMq7n1eUSs1KzS9u5Razrt9PzU7latrV64+zUknPScs/dpBbEG82e5dHAwfiZVehUPIKIcq3By46Xbx4kaSkJKZPn84LL7yAwWCgc+fOjBs3jri4OCwWC4GBgQX2qVSpEmFhYQBERUXRvn37m9Zv2bIlf3316tVvWn/p0iXMZjM6nQxaV5Y1DHDj1a51+HTtMd5bcZAJ3epSwdkIgLlOMIm/b8RpQG+c+3Un+bsfyerUReXEQpRuNksW4fDic2S1akPy7Lmgv/U3mwfD4/n+z1OkZeYwqFkVOtb1yZ1xUhQ6ad0khBDiRjqtDkcbJxxtnArleBbFQnp2Guk5GdTyq0JsbGqhHFcIIaAQik5HjhxBo9HQqFEjXnzxRS5cuMCnn35KRkYGQ4YMAchvxXSNra0tCQkJAGRmZt603s7OrsB6R0fHm/bPzs4mOTkZF5d7/8bAzc3+fn+8EsvDw+HuG5UgD5LXw8OBip6OvPHLHt7/7TDvPx5K1Qp5vxMewbBrJ3TpgtOQx+G772DYMFXzqkHyFq1yk3fxYnh+FLRqhX71b3jcYoZIU7aZWZtO8Ns/4VT2dODDwfWp7OV4i4MVQ14VFUfmhIwEXlz/Ij8f+plgz2B+H7SahhX/28x0pe01lrxFS/IWLclbtEpbXigtma8XsEpH3uskb9GSvEWrtOX9Lx646NSsWTNWrlxJUFAQAM2bN0ev1/Puu+/y2GOPAdzUGkmj0WAy5Q6oZ21tfcvWStfW62/xLfu1WR0yMzPvK2tcXCoWi3Jf+5Q05xLPUL9yLVISstWOcs88PByIiUl5oGM4W2l5o3tdpq4+zMs/7eDlznUIrHjt5GiAX1fhNGww+uHDSTsXTvqLL991WveizFucJG/RKi959Wt/x3HYYHIah5I4ez6kmSGt4HHOR6cwc8tJriRm0KVeJfqFBKDXah7o9Sltry8UT+YNYWsZ/+cLxGbEMK7RK4xr/Bp6nf4/PW9pe40lb9GSvEVL8hat0pYXSl9myVu0JG/Rkrzq0Go1d2zgc//TGfyLs7NzfsHpmoYNG5KVlYXZbAYgOjq6wPrExESMxtwuUm5ubndc7+rqesv1QP425YVFsdBpaVs6zO1Aanb5a/bq7WLLW73q42jUM/X3IxyKiL++0t6epLmLMPUbgN0H72H/+iuQ9/snhLgz/eYNOA5/kpx69UlasATsC540LBaFlfsieGfFQTKzzUzsHszjzaqgt3rgU4j4l0RTAs9vHsUTawbganBjfb8/mNDkTRnAVQghhBBClEoPfMVw7tw5zp8/X2DZtaJQZmYm3t7eHDhwIH+doigcP34cT8/c2Y1q1KhRYD3AsWPHCqw/fvx4gVZNR48exWAw3NTtrqzTarR83PJzdkTuYOBvfUjNKv1V0fvl7mDgzV71qOhk5LN1x9h19oaCpF5PyvRvSX9mDMbZ3+Ew6mm4z9ZwQpQ31lv/wHHIIHJq1iZp4TIUh4Lvq9HJGUxedYjFe8MIqeLOB482orZP4QyEKgpaH7aWFgubsPT0r4xr/Cob+2+lrkd9tWMJIYQQQgjxnz1w0WnOnDl8/PHHBZatWLECa2tratWqRceOHVm0aBHJyckArFu3jtjYWJo3bw5Ax44d2blzJ0eOHAHg8uXLbNmyJX9969atyczMZMGCBQBkZ2ezaNEimjVrlt/NrjzpVb0vC/ouYF/UXh79rTfJmUlqRyp2TkY9r/eoR1VPB2ZsOsmW41eur9RqSXvnfVLfnoxh1XKcHuuLJrn8vUZC3AvrHdtxenIg5qrVSfp1OYqTc/46RVH46+RVXl+8n8j4NJ5tW4Pn29XEzubWM9mJ/y7RlMBzm0Yy+MbWTaGTpHWTEEIIIYQo9R54TKcBAwbw2GOP8dZbb1GvXj327NnDihUrGD58OI6OjowYMYLVq1fTr18/6tevz9q1a2nTpg116tQBcotKTZs25emnn6ZNmzbs2LEDV1dXHn30USC3e92zzz7Lxx9/zP79+4mMjOTMmTO89957Dxq91Opfuz+pKVmM3DiEAat7s7DbMpxsnNWOVazsbKx4rWswX244zpy/zpCWmUP3Br756zOeG4vFwwOHF5/DqVdXkhYsRfHyUjGxECWL1Z7dOD3eH7OvH4mLV6K4uuWvSzFlM2frGfZeiKVGRSdGtwnC3cGgYtqya33YWl7+8wXiTLGMb/waLzV6RYpNQgghhBCizHjglk5169Zl2rRp7N27l7fffpujR48ydepUXnnlFSB3zKalS5dSv359Tp8+zVNPPcXnn3+ev79Go+Gbb77h8ccf5/Tp04SGhrJgwQLsbxhT5Nlnn2Xy5MlER0fj5OTE3LlzqVu37oNGL9W6Ve3B7I5zORxziP6repJoSlA7UrGzsdbxUqfaNK3qwaLdF1i46zyKcn2g+MxHHyNp3iKszp/DpWt7dOfPqphWiJLD6sA+nB7ri7lCBZKW/obi4ZG/7nBkPBN/3cf+8DgGNqnM693rSsGpCCSY4vNbN7kZ3Vnf9w9eC31DCk5CCCGEEKJM0Sg3XqWXcWVh9jooOMr9hrC1PL1uMDXcarG4+wpcDK4qp7tZUY/Kb7Eo/LT9LJuPX6F1zQoMbVEdrfZ610ur/f/gNKg/aDQk/bKEnPp3nnK8tM0iIHmLVlnLqztyGOc+3VCcnElctRaLtw8AWTlmFu66wIajl/FxseXZtjXwd7/9LBTFlbcketDM68PWMv7PscSb4nih4fgib91U2l5jyVu0JG/RkrxFq7TlhdKXWfIWLclbtCSvOop89jqhrg4Bnfmx83xOxZ+g76oexJvi1I5U7LRaDUNaVKN7A1/+OHGV6ZtOkGO25K/PadiYxNUbUIy2OPfqivUfm1VMK4R6dCeO49y/B4qDA4nLfssvOIXFpvLm0gNsOHqZjsE+vNe3QbEUnMqbBFM8z24aweA1A3A3ekjrJiGEEEIIUeZJ0akMaOffkZ86L+BMwin6rOxObEas2pGKnUajYUCTygxsWpk952P5dN0xTNnm/PXmqtVJ/H0j5oDKOA3qj83SX1VMK0Tx0505jXPf7ih6GxKX/obFzx+LReG3A5G8vewA6Zk5vNY1mMEPVUVvpVM7bpmz7sIaWixswoqzSxnf+DU29PuTYI96ascSQgghhBCiSEnRqYxo49eOeV1+5XziWfqu7EZMeozakVTRrb4vwx6pztGLCXy4+ghpmdn56ywVKpK4ai3ZoU1xfGY4xm+mq5hUiOKjPX8Opz7dcruYLluNpXIVYlNMTPntMIt2X6BhgBtTHm1EsK+L2lHLnGutm55cO1BaNwkhhBBCiHJHik5lyCO+rZnfdTFhyRfos7Ir0enRakdSReuaFRnTribnY1J4f9VhktKz8tcpjk4kLVxGZree2L/1OnbvvAkWyx2OJkTppo0Ix7lvdzQ52SQu/Y2cqtXYfjqKiYv3ERabysjWgYxtXxMHg7XaUcscad0khBBCCCHKOyk6lTEtKj3Cgq5LiUyJoPeKLkSlXVU7kipCq3rwcufaRCVl8O6Kg8Qkm66vNBhI/v5HMoYMw3bGNBzGjIbs7NsfTIhSSnvpIs59uqNJSyVx8SqSK1djxqaTfLPlFL6udkzp35CWQRXQaDR3P5i4ZwmmeJ7ZOJwn1w7Ew+gprZuEEEIIIUS5JUWnMqi5z8Ms7LaMS6mX6LWyC1dSL6sdSRXBvq5M6FaXFFMO7648yKX4tOsrdTpSP/yMtNfewLB4IY5PDoS0tNsfTIhSRht1Fae+3dEkxJP06woOOVdiwq/72Hshlv6hAUzqUQ9PR6PaMcuctRd+5+EFoaw8t4yXG09gfb8/pHWTEEIIIYQot6ToVEY19W7Oou7LuZp2lV4ru3A59ZLakVRRvYIjk3rWxWJReG/VIc5F3zAlpUZD+vjXSPn0S/R/bMa5bzc0ceVv9j9R9mhiYnDq2x1tVBRx85fwY7oTU1cfwWCt43+969OzoR9arbRuKkzxpjie2Ticp9Y+hqetF+v7/cmroa9L6yYhhBBCCFGuSdGpDGtSsSm/dl9OTHoMPVd05mJKpNqRVOHnZs+bvepjtNbxwW+HOX4pscB60+AhJM+Zh9XxYzh3aw+nT6sTVIjCEBeHc78e6C5Gcmbmz0yIsGLd4Uu0r+3N5L4NqezhoHbCMmfthd9psaAJK88t45WQibmtm9zrqh1LCCGEEEII1UnRqYwLqdCExT1WkGBKoNfKrkSmRKgdSRUVnIy82bM+bvY2fLzmCPsuxBZYn9WlG0m/rkAbGws1a+L45GNY//UnKIo6gYX4D7RRV6F9e3QXzrHh7a8YF2FDSkY2L3euw1MtqmFjrVM7YpkSb4pj9MZhBVo3vRIyUVo3CSGEEEIIkUeKTuVAI68QlvRYSVJmIr1WdCE8OUztSKpwtbdhUs96+LraM23DcbadiiqwPrtpcxL+2gUTJmC9dxfO/Xrg0iIUw5zvITVVpdRC3J320kXsJ76Ma+NglGPH+G70ZD7L9KKenysfPNqI+v6uakcsc9acX02LBU1YdW65tG4SQgghhBDiNqToVE7U92zI0h6rSMlKpteKLlxIOq92JFU4GKyZ2D2YGt7OfPvHKdYfKTjWlaWiN7z/PnEHTpD85UwUoy0OE8bjVq8GdpNeQ3f+rErJhbiZNjwM+/Ev4BpaD8NPczjbphvPvfQNv3vWYvgj1XmxYy0cjdLqpjDFm+IYtGwQQ9Y9Lq2bhBBCCCGEuAspOpUjdT3qs7TnajJy0um9oivnE8tnAcWot+LlznVoFODG3L/PseyfcJR/d6MzGMgcOIjEDX+S8PtGstp3wDjne1ybNsTxsb7oN28Ai0WdH0CUe7rzZ3EY+wyuTRtgWDSf05368tyrP/B8s6E41K7J+/0b0apmRTQaGSy8sGSZs5hz9HseXhDKr8d+5ZWQiWzo96e0bhJCCCGEEOIOpOhUzgS712Vpj9Vkmk30WtmVswln1I6kCr2VlrEdatEi0Itl/4Qz9+9zWG41fpNGQ05IE1K+mUP8geOkvTwBqyOHcXqsHy7NG2H87ms0yUnF/wOIckl36iQOo4fh0rwxNiuWcrLn44x+7UfGhjyBTdXKTOpRlw8HN6WCk1HtqGVGjiWHBSfm0eyXhkz4azxVnavxz4h/eCVkItY6a7XjCSGEEEIIUaJJ0akcqu1eh2U9fyfHkk2vlV04k1A+Z2vTaTWMaB1Ix2AfNhy9zHd/nMJsuf3A4RavCqS/+jrx+4+R/M1sFFc37CdNwK1uDexffQndqZPFmF6UJ7qjR3Ac9iQuLZugX7eGY/2GMPLVH3mx/gBsA/yY1KMur3evSw1vZ7WjlhkWxcLyM0tosTCUF/54FjeDGwu7LWNVr3XUq1BP7XhCCCGEEEKUClZqBxDqqOlWi+U919BnZTd6rejCsp6rCXKtoXasYqfVaHiieRXsDVYs3RtOepaZdx4LufNOej2ZffqT2ac/VocOYJz1LYYF8zD+OJusFq3IGD6KrA6dQCczhYkHY3VwP7affYTNujVYHBw48thIpgW25aLWlto+zkxq7E+Nik5qxyxTFEVhXdgapu6ezIn4Y9R0rcWPnX6hc+Wu0l1RCCGEEEKI+yRFp3IsyLUGK3rlFp56r+zC0h6rqelWS+1YxU6j0dC7kT92eit+/vscT331Bw39XQmp4k6Nis7otLe/0Myp14CUr74h9e3JGOf9iOHH2Tg99RhmP38yhgzHNGgwiovMHCbuj9We3dh99iH6LZuwODtz4MnnmVatNVewobaPM0Ok2FToFEXhz8gtTN3zHgei91PFqSrftJ9Nr2p90WqkUbAQQgghhBD/hRSdyrnqLoGs6PU7vVd2o8/Krizp8Ru13euoHUsVHYJ98HIysuNcDH+dimLTsSs4GKxpGOBGSGV3aldyxlp364tPxd2d9BdfJv35F9Gv/R3j7G+xf/dN7D56H1PfR8kYNgpzneBi/olEqaIoWO/Yju1nH6HfthWzqxv7hr3EtIAWRCl66vg4M7yxP0FSbCp0uy7vYMrud9l1ZQeV7H35ovUMHg16DCutnCKFEEIIIYR4EPKJWlDVuXpui6cVeYWnnr+V2xmZ6vm50q6RP5GXEzkcGc/e87HsPhfD1pNXMep1NPB3I6SKO3UruWBjfYvuc1ZWZHXvSVb3nuiOHcU45zsMSxZhnP8zWU2b53a969wNrGUAYpFHUbD+cwt2n32E9e6dmD292DvqVab5PUSMWUcdb2dGSrGpSByI2sfUPZP5I3IznrZefNDiE56o9RQ2Ohu1owkhhBBCCFEmSNFJAFDFqSrLe/1On5Xd6LuyG0t6rKKuR321Y6nGYK0jtIoHoVU8yMqxcOxSAnvPx7I/LI4dZ6KxsdJS18+VkMru1Pd3xVZ/85+SuXYdUj/9krRJ/8PwyzyMP3yP0/CnMFf0xjRkGBlPDEHx8FDhpxMlgqKg37gO288/xnrfP+RU9GbXc6/zlXdTYs1a6lR05plG/gRKsanQHY87xtQ9k1l34XdcDa683WwyQ+sMx9baVu1oQgghhBBClClSdBL5KjtVyR/jqe+qHvzabTkNvBqpHUt1eistDfzdaODvRo7ZwskrSew9H8s/F2LZez4WK62GOpVcCKniTsMANxwMBVsxKS6uZDw3lozRz6HftAHjrG+w++A9bD/9kMxefckYPoqc+g1V+ulEsbNY0K9ZnVtsOnKIHF8/drzwNl95NSY+R0Odis48K8WmInEu8Qwf7ZnCirPLsNc78FroG4yq+yz2ege1owkhhBBCCFEmSdFJFODvGMCKXmvovbIb/X/rxaLuy2jkdZfZ3MoRK52WOpVcqFPJhacersaZqGT2no9l74VYDkbEo9VALW9nGldxp3Fld5xt9dd31unI6tiZrI6d0Z05jXH2t9gsWoDh1wVkN2pMxrBRZPboDXr97QOI0stsxmbVcmy/+ASrE8fJrlyVbS9PZoZbAxKyFYIruPB8Yz8CK0ixqbBFJIfz2T8fsejUL9jobBjbcBzP1h+Di0EG+RdCCCGEEKIoSdFJ3MTXwY8VPXO72vVflVt4CqnQRO1YJY5WqyGoohNBFZ0Y1LwKF2JS2ZvX+unHbWf5adtZqldwJDSvAOXuYMjf11w9kNSpn5L2xtsYFs7HMPs7HJ8dgeXtN8h4ciimp57GUqGiij+dKDQ5Odgs/RXbaZ9idfYM2YE12DrhQ752CiYx20KwlzNjpNhUJK6mXeHzfR8z7/hPaDVahgePYkzDcXjaeqodTQghhBBCiHJBik7ilio5+OZ3tXv0t94s7LaMJhWbqh2rxNJoNFTxdKCKpwOPhgZwMSE9twXU+Vjm7TjPvB3nqexhT0gVd0Ire1DB2QiA4uBIxohnyBg2Cus/t2Cc/S22n32E7bRPyezek4xho8kJCQWNRuWfUNy3rCwMvy7Adtqn6MLDyKpVhy2TPmOmfU2Ss8wEV3DihUb+VK/gqHbSMicuI46vDnzOnCPfkaPk8HiNJxnX+BW87X3UjiaEEEIIIUS5IkUncVve9j55Xe26MuC33izotoRm3g+pHavE02g0+Lra4etqR5/G/lxNzMhvAfXr7jB+3R1GJVdbQiq7E1rFg0qutmi0WrLbtCO7TTu0589h/GEWhgXzMCxfSnbd+mQMG0lm735gMNw9gFCXyYThl7nYTv8C3cVIsuo14I+h4/nGWJ2UzByCvRzpI8WmIpGUmcjMg1/x7eGZZOSk0y9wAOMbv0ZlpypqRxNCCCGEEKJckqKTuKMKdhVZ0TO3xdNjq/syv+tiHvJpoXasUqWCs5HuDXzp3sCX2BQT/1yIY++FGFbsi2D5vggqOBlpXNmdkCruVPGwx1KlKmnvfUDaa29gWLII45zvcHzhWSzvTML0xBAyhgzDUslX7R9L/Ft6Osa5P2Cc8SW6q1fIbBzKptFv8L11ACmZOdT1dKBPY3+qeUmxqbClZqcy6/A3zDj4JUmZifSo2ptXQ14n0DVI7WhCCCGEEEKUa1J0EnflZVeB5b3W0HdlNx7/vR9zuyyiZaVWascqldwdDHSq60Onuj4kpmexLyy3BdSaQ5GsPhiJm70NIZXdaVzFnUAvR0xDhmF66mms/96Gcda3GKd/gXH6F2R17kbG8FFkN39Y7R9JpKZi/GEWtjO/Qhsbg6nZw2x4aTKz8CY10yzFpiJkyjHx07HZTNv/GbEZMXTw78RroW8Q7FFP7WhCCCGEEEIIpOgk7pGnrSfLev5Ov1XdeeL3R/m5y0Ja+bZRO1ap5myrp20tb9rW8ibVlM3+sDj2Xohl8/HLrDtyCSejdX4LqBrNHib74ZZoIyMw/jgbw7wfsfl9FTk1a0Gf3hgN9licXVBcXbG4uKK4uOTeOjmDTqf2j1omaZKTcguB385Am5BAxiOt2dxjKD+YPUk15VDP15HeUmwqElnmLBacnMdn/3zElbTLtKjUigmhb8iEB0IIIYQQQpQwUnQS98zD1oNlPX+n76ruDF4zgJ86/0Ibv/ZqxyoT7A3WtKxRgZY1KpCRlcPBiHj2no9l++koNh+/gr2NFQ0C3Ait4k6d19/G+uUJGJYvwTDne/jgA+wtllseV9FoUJyccgtQrq65hSkXVyyurijOLgULVNfWu7qi2DvI4OW3oUmIx/jt1xhnfYs2OYmMdh3Z2P0pfsp0JTUth3q+DlJsKiJmi5klpxfx8T9TiUgOo7FXKDPafcfDPi3VjiaEEEIIIYS4BSk6ifviZnRjWc/f6LeqJ0+ueYwfO8+nnX9HtWOVKUa9Fc2qedKsmidZOWYORyaw93ws+y7Esu1UFAZrHfX9XQlt0om6/R/Ht4IjsecuoklIQJsQjzYhPv++Jj4ebWICmoR4tPHxaONi0Z45k/s4Jfm2GRQrq9yilKtrbpHqWmEq737u7c0FK4zGYnylipcmJgbbb6ZjmPM92rRU0jt3Y0PXp5ib5kBqUg71/Bzp08iPqlJsKnQWxcJv51bw0Z4pnEk8TbB7PX7pupi2fh3QSHFUCCGEEEKIEkuKTuK+uRrcWNpjFY/+1pshawcxp9NcOgR0VjtWmaS30tG4sjuNK7uTY7Zw7FJibgEqLI5dZ2Ow1mkJ9HbC1kqHo9EaB6M7jl4VcfTX42i0xslojaNRj53BCu2/L86zs9EkJhYsUiXEo71FwUoXEYHV4UO5yzMybptXMRpv3ZoqryhlcXUFbw/0qVm53f60GtDpULS63Mc6HWi1eY+1+csUzfX7udtrQfvvZXn75C3/9zHRav9b660rV7B7532MP88Bk4n0Hr1Z33kw85OMpMZLsakoKYrCxvB1fLB7MsfijhDkUoPZHefSrUoPKTYJIYQQQghRCkjRSfwnLgZXFndfwYDVvRm67glmdfyZzpW7qh2rTLPSaann50o9P1eGWhROXUninwuxRKVmciUxnVNXs0kxZaMoN++r0YCjwRqHvCKUo9EaR4M1TkY9jkZHHD3dcPTX5623xmitu/1FfUZGbjEqPv7mglV8PJrEvBZX8fFoT5/EOm+ZJicn/xBORfQa3Y1yY6Eqv7h1rVil+9d6HYpOC5cvYczJIa13f9Z2HMTCeGtSo3Oo7+dIbyk2FQlFUdh2aSsf7H6PfVF7CXCszIy239Gnen90WhmjTAghhBBCiNJCik7iP3M2uLC4+0oGrO7NsPWD+a79j3Sr2kPtWOWCTquhlo8ztXyc8fBwICYmBQCLRSE1M5vkjGv/skjOyCYp736KKXd5WEwqyRlZpGeZb3l8a52mQHHq2v1rRSknowFHT38c/avhYNCjt9LeObCioElNQRMfj5sNxMcko7GYwWIBsxnMltzH5rx/lhsf522jWNCY/72N5fpjs/mGY+Ytt5iv72Ox5D2+YR8l977m2vPk7X99HzNK164sa9iZX2O0pF7JKzY19qeqp0Nh/7cKYPeVXUzd/R5/X96Gt50Pn7b6koFBg7DWWasdTQghhBBCCHGfpOgkHoijjRO/dl/BwNV9GbHhKb5tP4ce1XqrHavc0mqvFYv097R9ttlSoDh1rSiVnJGVV6jKvX8xIZ3kjGyyzbcesNyo1xUoTl3/d8Njgx5HD2/svJ1JcU3GYlEw5/2zKHn3824t/15+q+3M1x/fbjtL/jEtuTWlG5bnWCz/2o687a7vZ7EoRCakk3Ixmwb+zvRqJMWmonIo+gBT90xmc8RGPIyevP/whwyuNRSDlUHtaEIIIYQQQoj/SIpO4oE56B1Z1G0Zj/3ej1Ebn8aiWOhVva/ascQ9sNZpcbO3wc3e5q7bKopCZo7lptZTN7aqSsnIJibFxNnoFFIysrDcoqufGrSa3NZhWo0m91arQafVovvXcp1Wm7sufztoWNmdtjUqUEWKTUXiWPQxXlv/Or+fX4WzjTOTmr7DsOCR2FnbqR1NCCGEEEII8YCk6CQKhb3egQXdljLo9/6M3jQMs2Kmb+CjascShUij0WCw1mGwNuLpePdZ6iyKQlpmToGWVMkZ2ej0VpgysvKKPLkFHm3e/evFn4KPtRoNVtp72E6bt90N+2nzbv+rG7svigdntpg5EL2PTREb2BK+kYMxB7C3duDlxhMYXe85HG3UGvFLCCGEEEIIUdik6CQKjb21Pb90XcLgNQN4bvNIzIqZR4MeUzuWUIlWo8HBYI2DwRofF9v85VLEKX9iM2L5I2ITmyM28mfkZuJN8Wg1Whp5hfB+m/fpG/A4rgY3tWMKIYQQQgghCpkUnUShsrO2Y16XXxm8diBjNo/GolgY4zFa7VhCiGJkUSwcjN7P5oiNbA7fwIHo/SgouBvdaeffkbZ+7Wnl2wYXg6sUIYUQQgghhCjDpOgkCp2ttS3zuiziqbWP8cKWZ9kfv5sWXm1oWakVTjbOascTQhSBeFMcf0ZuYXP4Rv6I3ERsRiwaNDT0asQrIRNp69eeep4N0GruMtOhEEIIIYQQosyQopMoEkYrIz93Xsjr215h+Ynl/HjwR3QaHY28Qmjj1442fu2o61FfLkCFKKUsioUjMYfYHLGRTeEb2B/9DxbFgqvBlda+7Wjr357Wvu1wM0q3OSGEEEIIIcorKTqJImOwMvBZ66/4od8s1h39gz8iNrIlYhNT90xm6p7JuBvdaeXbljZ+7Wjl2xZ3o7vakYUQd5BoSmDrxT/YFL6BLRGbiMmIBqCBZ0NeavQK7fw7UN+jITqtTuWkQgghhBBCiJJAik6iyFlprWhSsSlNKjZlQpM3iUmPYevFLWyJ2MSfkZtZcnoRGjTU86hPG792tPZrTyOvxlhp5ddTCDUpisLRuCNsCd/IpogN/HN1D2bFjLONM61929LWvwOtfdvhYeuhdlQhhBBCCCFECSRX9aLYedh60C9wAP0CB2BRLByOOciWiE1sidjEF/s/5bN9H+Nk48wjlVrnFqF821LR3lvt2EKUC8mZSWy9+CebwzewJXITV9OuAFDXoz5jG75EW7+ONPRqJEVhIYQQQgghxF3JVYNQlVajpb5nQ+p7NmRc41dJNCWw7dLW/CLUqnPLAajpWjt/LKjQik2x0dmonFyIskFRFE7EH8/rMreRPVd3kWPJwVHvRCvfNrTz70Brv3Z42XqpHVUIIYQQQghRykjRSZQozgYXulftRfeqvVAUhZPxJ/IKUBv57vDXzDg4DVsrO1pUakkbv/a08WuHv2OA2rGFKFVSs1LYevFPtkRsZHP4Ri6nXQKgtlswz9V/gbZ+7WlcIVRaMwkhhBBCCCEeiFxRiBJLo9FQ060WNd1q8VyDsaRmp/L3pW25F8oRm1gfthaAqs7VaOOb2wqqmffD2FrbqpxciJJFURROJ5zKb82068oOsi3Z2Fs78Ihva17xm0gbv3bSjVUIIYQQQghRqKToJEoNe2t7OgZ0pmNAZxRF4ULSufxuePNO/MT3R77BRmdDM++Hcrvi+banuksgGo1G7ehCFLvU7FS2X/yLzREb2Ry+gYupkQDUdK3FqHrP0davPSEVmqDX6VVOKoQQQgghhCirpOgkSiWNRkMV52pUca7G8LqjycjJYNflHWyJ3MQfEZt46+/XeYvX8XXwo3VeK6gWlVrioHdUO7oQRSIpM5GT8Sc5c/YoK4//xs7Lf5NlycLO2p6WlVrxYqOXaevXHh+HSmpHFUIIIYQQQpQTUnQSZYLRykhrv7a09msLD31AZEoEf0RsZkvEJpadWczPx+dgpbUitELT3Bnx/NpRxy1YWkGJUic9O50zCac4EX+ck/EnOBl/nJNxJ/LHZQIIcqnBsOBRtPPvQJOKzaQ1kxBCCCGEEEIVUnQSZZKvgx9P1h7Kk7WHkm3O5p+oPWwO38iWyE1M3vU/Ju/6H562Xnnd8NrxiG9rXAyuascWIl+2OZtzSWc5GXeck/HHOZFXYApLuoCCAoCNzobqLkE093mYGq61qOlak5ZBzbDJdFI5vRBCCCGEEEJI0UmUA9Y6a5p5P0Qz74eY1Ox/RKVd5Y/IzfwRsYn1F9aw8OR8tBotDTwb5XbDq9oMm2wHPG298LD1lFYiokhZFAvhyWG5rZbyCkwn409wNvEM2ZZsAHQaHVWcqlLHvS79AgfkFZhqEeBU+aYZ5jwcHYiJSVHjRxFCCCGEEEKIAqToJModL7sKDKwxiIE1BmG2mDkYsz9vQPKNfLJ3Kh/vVQps72pwzStAeeFl64Vn3r8b73vaeuJs4yLd9cRtKYrC1bQr/+oWd5xTCSfJyMnI387PwZ8arjVp79+JGm41qeFai2rO1TFYGVRML4QQQgghhBD3T4pOolzTaXU08gqhkVcIr4RMJMEUT5I2mlOXLxCdHkVU2lWi06OITo8mKv0qe67sIir9KpnmzJuOpdfq8wtQnnYV8DTm3bf1wsuuQv59D6OnFBDKuLiMuLwWS8c5EZdbYDqVcJKkzMT8bTxtvajhWosnaw2lhmstarjVJMilBvZ6B/WCCyGEEEIIIUQhkqKTEDdwMbgS6OFPgL7GbbdRFIWUrGSi0qPyClK5/258HJ4Uxj9XdxObEXvLYzjbON/QSqpgiykv2wr5j10NrtJ6qgRLzUrJa7V04vq4S3HHicmIzt/GycaZGq416VWtLzVca1LTtRZBrjVxM7qpmFwIIYQQQgghip4UnYS4TxqNBkcbJxxtnKjuEnjHbbPN2cSZYgu0mMotUF3Nv78/6h+i06NIz0m/aX9rrTUeRs8CLaY8rt23zW09VUVTifQUMzY6A0YrAzY6A9ZaaylWFSJTjokziafzxlw6kT/uUmRKRP42tla2BLnWoJ1/h9yWS641qelWCy/bCvJ/IYQQQgghhCiXpOgkRBGy1llTwa4iFewq3nE7RVFIy069qcXUtW590elRXE67zMGYA8RmxGBRLHc8nlajxaAzYrCywaAzYpN3a7QyYGNlwKDLvTXqDBisjNjobDBYGTHoDBisri8z3rDuWkHLkHdrtDLkHTtvH53hpkGtC4OiKGRbssmyZJFtziLLnEWWJYsscyZZ5myyLVlkmjPJNmfferklO3cfc9Y9bZttvn4/05xJUnYCZ+PP5r/m1lprqjkHElIhlMG1huQXmPwc/dFqtIX+8wshhBBCCCFEaSVFJyFKAI1Gg73eAXu9A1Wcq91x2xxLDnGmuNyiVNpVMGQTHZ9AhjmDzJxMTDkZZJpNZOSYMF1bZs4gI8dEptmEKcdEclYSGelRedvm7mPKuzUr5v/8c1hprW5Z7LpWlDJYGTAabEjNSM8vBOUXkyxZ/yoOXV9e2HQaHXqdHr3OBmutNTZ5t9eW6bXWWOv0GKyM+Lr40K1yL2q61qKGay2qOFXFWmdd6JmEEEIIIYQQoqwpNUUni8XCl19+yeLFi9FqtYwYMYInn3xS7VhCFDsrrRVeebPn4V4XDw8HYmJSCu342ebs/KLVtSJVbkHLhMlsIjPn5oKWKf82dxtT/r4Fl8Wb4iBLQatYodfpsbO2w0XngrVWj41Oj7VOn18AstbpsdHaYK2zRq/NKwbprPO2vfXyGwtGtysk6bV6dFrdPb8ehf36CiGEEEIIIUR5UWqKTl999RWzZs3i5ZdfxsfHh7feegt3d3e6dOmidjQhyhRrnTXWOusim0VNijhCCCGEEEIIUT6UiqJTamoqs2fPZvTo0QwZMiR/2YwZM6ToJIQQQgghhBBCCFEClYpRb/ft20dmZibdunXLX9a6dWvOnj1LVFSUismEEEIIIYQQQgghxK2UiqJTdHQ0NjY2+Pv75y9zdnbGwcGBiIiIO+wphBBCCCGEEEIIIdRQKopOJpMJBwcHNBpNgeW2trbEx8erlEoIIYQQQgghhBBC3E6pGNNJr9ej1d5cH9NoNJhMpns+jpubfWHGUpWHR9EM8lxUJG/RkrxFS/IWrdKWF0pfZslbtCRv0ZK8RUvyFr3SllnyFi3JW7Qkb8lTKopObm5uxMfHYzab0emuT3WelJSEra3tPR8nLi4Vi0UpiojFqrTN/iV5i5bkLVqSt2iVtrxQ+jJL3qIleYuW5C1akrfolbbMkrdoSd6iJXnVodVq7tjAp1R0rwsKCiInJ4fDhw/nLzt37hwZGRl4enqqmEwIIYQQQgghhBBC3EqpKDr5+vpSu3ZtZs2alb9s3rx5ODk5Ubt2bRWTCSGEEEIIIYQQQohbKRXd6wBeffVVhg0bxuDBgzEajWzdupWJEydiZVVqfgQhhBBCCCGEEEKIcqNUtHQCaNq0KfPmzUOv15OQkMDkyZMZMmSI2rGEEEIIIYQQQgghxC2UqmZCDRo0YPbs2WrHEEIIIYQQQgghhBB3UWpaOgkhhBBCCCGEEEKI0kOKTkIIIYQQQgghhBCi0EnRSQghhBBCCCGEEEIUOik6CSGEEEIIIYQQQohCJ0UnIYQQQgghhBBCCFHopOgkhBBCCCGEEEIIIQqdFJ2EEEIIIYQQQgghRKGTopMQQgghhBBCCCGEKHRWagcoTlqtRu0Ihaa0/SySt2hJ3qIleYtWacsLpS+z5C1akrdoSd6iJXmLXmnLLHmLluQtWpK3+N3tZ9AoiqIUUxYhhBBCCCGEEEIIUU5I9zohhBBCCCGEEEIIUeik6CSEEEIIIYQQQgghCp0UnYQQQgghhBBCCCFEoZOikxBCCCGEEEIIIYQodFJ0EkIIIYQQQgghhBCFTopOQgghhBBCCCGEEKLQSdFJCCGEEEIIIYQQQhQ6KToJIYQQQgghhBBCiEInRSchhBBCCCGEEEIIUeik6FRKWCwWvvjiCx566CFatGjBzz//rHake7J9+3batWundoy7iouLY+zYsTRo0IC6desyevRo4uLi1I51Rzt37uT1119n3Lhx/Prrr1gsFrUj3ZOcnBz69OnDV199pXaU21q9ejVBQUEF/rVs2VLtWPfEYrHQp08fxo8fr3aU29q9e/dNr++1fxMmTFA73i1dvXqVYcOG0bhxY5o0acLYsWOJiYlRO9ZthYeHM2bMGBo2bEjz5s355JNPyM7OVjvWTW53jlizZg0dO3YkJCSEN998k8zMTBXS3exO57SoqCgeeughLl68WMypbu9Wec1mMx9//DHNmjWjTp069OzZk8OHD6uUsKDbvb4XLlxgypQpvPDCC0yfPp2UlBQV0t3sXj7jvP/++wwePLiYEt3drTJHR0ff8v04PDxcpZTX3e01njJlCu3atSMrK6sYU93erfLe7nzXpk0blVJed6u8WVlZvP322zz00EM0aNCAgQMHluj3iLS0NCZPnszDDz9MgwYNePHFF4mOjlYpYa67XVeUtHPcvVwHlaRz3J3ylsRz3N1e35J6jitMUnQqJb766itmzZrFiBEjeOutt5g5cyZr1qxRO9YdnTt3jvHjx5eKYsiLL77IkSNHeOmll3jppZfYu3cvEydOVDvWbf3++++MHj0aKysrXF1def/995k8ebLase7J7NmzOXbsmNox7ujYsWO0aNGCJUuW5P/77rvv1I51T+bPn8+FCxd49dVX1Y5yW7Vr1y7w2i5ZsoRff/0VFxcXatasqXa8Wxo3bhx6vZ7p06fz7rvvcu7cOZ577jm1Y91SQkICgwYNIisri5kzZ/Lqq6+yePHiEveedrtzxI4dOxg/fjxNmjThs88+48yZM0yZMkWllNfd6ZyWmprK2LFjiY2NVSHZrd0u74wZM5g/fz6PPvoo77zzDpmZmYwePZr09HSVkua6Xd6wsDD69evHxYsX8fPzY8mSJQwePJicnByVkua6l884+/fvZ968ecWY6s5ul/n48eO4urre9L5csWJFlZLmuttrfOLECebNm8eECRPQ6/XFnO5mt8v779d1yZIlNGvWTPXz3Z3eI3bv3s3kyZOZMWMGzs7ODBkyhPj4eJWS5rpd3jFjxrBt2zbeeecdpk2bRnh4OIMHDyYjI0OlpHe+riiJ57i7XQeVtHPcnfKWxHPcnfKW1HNcoVNEiZeSkqIEBwcrX331Vf6yZcuWKV26dFEx1Z0dOnRICQ0NVfr27au0bt1a7Th3tGPHDqV+/frKlStX8pfNmTNHqVGjhpKamqpislvLzMxUmjVrpixZsiR/2dy5c5Xg4GAlKytLxWR3d/bsWaVOnTpKw4YNlS+//FLtOLf11FNPKTNmzFA7xn2LiopSGjZsqMycOVPtKPdt+fLlysMPP6yYTCa1o9wkIiJCCQwMVGJiYvKXbd++XQkMDFSioqJUTHZrM2fOVJo2baqkp6fnL1u2bJkSGBioXL16VcVk193pHDFgwABl8ODB+Y8jIiKUWrVqFXj9i9ud8sbFxSk9evRQ+vbtqwQGBiqRkZEqpbzudnlTUlKU+vXrKxs3bsxfdvToUSUwMFD5888/1YiqKMqdX99nnnlGefXVV/Mfnzp1SgkMDFT27t1b3DHz3ctnHJPJpHTs2FFp2LCh8sQTTxRzwpvdKfOMGTOUp556Sp1gt3G319hsNiv9+/dXhg4dqkK6m93P596IiAildu3ayokTJ4op3c3ulLdVq1bK77//nv84IyNDqVmzpvLbb78Vd8x8t8u7b98+JTAwUDl8+HD+ssjISCUwMFBZtWqVGlHvel1R0s5xd8tb0s5xd8pbEs9xd3t9S+I5rihIS6dSYN++fWRmZtKtW7f8Za1bt+bs2bNERUWpmOz29u7dy2uvvcbjjz+udpS7qlOnDosXL6ZChQr5y1xcXLBYLCWylZbJZGLs2LH06tUrf5mXl1eJaVp+OxaLhddff51OnTpRq1YttePc0fHjx6lTp47aMe7blClTcHFx4emnn1Y7yn0xm83MnDmTUaNGYWNjo3acm1z7dldRlPxl17qqGQwGVTLdydGjR6lXrx5GozF/WUBAAACXLl1SKVVBtztHpKWlcejQoQLnO19fX6pWrcquXbuKO2a+O53Tzp49S0hICJ9++qkKyW7tdnn1ej1z584t0D3F1dUVQNXul3d6fTt27MjYsWPzH3t5eQElN+8106ZNQ1EUBg4cWIzJbu9OmUviOe9ur/HChQs5duwYr7/+ejEnu7X7+dw7c+ZM2rRpQ40aNYoh2a3dKW9cXFyB811OTg4Wi0XV893t8h49ehR7e3uCg4Pzl1WqVAm9Xq/a+e5u1xUl7Rx3t7wl7Rx3p7yKopS4c9zdXt+SeI4rClJ0KgWio6OxsbHB398/f5mzszMODg5ERESomOz2hg4dSp8+fdSOcU8cHByoVq1agWXbtm2jWrVqODg4qJTq9hwdHRk4cCA6nQ7ILUL98ssvtGrVCmtra5XT3d5PP/3E5cuXmTRpktpR7ujixYskJSUxffp0GjRoQLNmzXj33XdJTU1VO9od7dy5k7Vr1+Ll5cXEiRP5/PPPVW8Kf6/++OMP4uPj6du3r9pRbikwMBBXV1emTp1KQkICkZGRTJ8+ndatW+Po6Kh2vJtotVqSkpIKLDt79iwAnp6eakS6ye3OEbGxsVgsFgIDAwssr1SpEmFhYcWU7mZ3Oqc1btyYSZMm5b8nlwS3y6vX628qLmzbtg2tVku9evWKK95N7vT69uzZEx8fn/zHc+bMwc3Njfr16xdTupvd7TPO4cOH+fnnn5k6dWqJKUzfKfPx48f566+/aNq0KQ0aNGD06NGq/r3BnfPGx8fz+eef4+3tzaxZs/jf//7HyZMnizlhQff6uTcuLo5Vq1YxdOjQYkh1e3fK26xZM7755hvCwsJITExk6tSpuLu707x582JOed3t8up0OjIzMwt0nYqIiCArK0u1892drivi4+NL3DnubtdBJe0cd7e8Je0cd7e8JfEcVxSk6FQKmEwmHBwc0Gg0BZbb2tqW2ItKrbb0/mqdPXuWtWvXMmTIELWj3NX06dPp0KEDsbGxqvcHv5Pw8HCmTZvGe++9h5OTk9px7ujIkSNoNBoaNWrEjBkzeP7551mxYgXvv/++2tHu6OOPPwZyP4zHxcUxZ84cevXqpfpgmvfil19+oXfv3gVa5pQkRqORr776ijVr1tC0aVPatWtHRkZG/mte0oSGhnLgwAFWrlwJ5H4AnzFjBoGBgVSqVEnldLlud44wmUwANxX8bW1tSUhIKPJct3Onc1pJPN/da6bMzEy++eYbunbtioeHRxGnur17ybtp0yZ69OjBzz//zLfffqvq+8Wd8mZlZfH666/z5JNP0qBBg2JMdWe3yxwfH8+lS5fw9vZm6tSpfPDBB4SHhzNixAjMZnMxp7zuTq/xt99+S3JyMqmpqcTExLB+/Xr69+/P33//XYwJC7rXv7nFixdTvXp11X837pT3gw8+IDExkY4dO9KkSRN+//13vv/+e2xtbYsxYUG3yxsSEkJ2djYfffQROTk5pKam8s4772BlZcUjjzxSzClv7cbripJ6jrvRv6+DSuI57kZ3um4rKee4G90ub0k6xxWFkv1bJIDcbyZv9Qev0Wjy37xE4TCbzUycOJHAwEB69+6tdpy7Cg4OJiQkhLCwMDZs2KB2nFtSFIU33niDzp0706pVK7Xj3FWzZs1YuXIlr732Gs2bN2fQoEFMnDiR1atXk5ycrHa8Wzp69CjHjh2ja9eurF27lh9//JEVK1aQkpJS4gdAj4iIYMeOHQwYMEDtKLeVlZXFO++8Q4MGDfj444+ZNGkSycnJjBkzRtWLstvp378/Dz30EK+++iqhoaF06NCBK1euMGjQILWj3dW1wYD//Y2qnO+Kxueff05CQgIvvvii2lHuytfXl+bNm6PRaJg3b16B7j8lyddff43FYikVrymAvb09ixcv5uuvv6ZVq1Z06tSJb775hsjISLZv3652vJuYzWaWLVtGQEAAGzduZPbs2WzevJmAgAA+/PBDtePdkaIoLFq0qESf7yD3fUGn0/HOO+/w0UcfUa1aNZ555hmuXr2qdrSbBAYG8txzz7FgwQJCQkJo2rQp27dvp2PHjri5uakd76bripJ+jitt10F3y1vSznF3yltaznH/lRSdSgE3Nzfi4+NvurhJSkpS9VuHsujrr7/m1KlTfPjhh1hZWakd564eeeQRPv30U8aNG8fkyZNv6lJTEsyfP5/IyMgSM+7C3Tg7OxMUFFRgWcOGDcnKyuLcuXMqpbqza02yb2yuX7VqVRo1asSJEydUSnVvVq9eTfXq1alataraUW5r8+bNxMXFMXv2bHr06MHgwYOZPXs2O3fuVPWb9duxsbFh9uzZzJ8/nwkTJuDt7Y2Pj0+p6PJ87SLh3y30EhMTy9y3fmrbuXMnP/30E6+//nqJaQF3J0FBQUyYMIG5c+eyYsUK/vrrL7Uj3eTEiRPMmTOHqVOnlojZ1O6FXq+nbt26Bb7c9Pf3x83NTfUua7cSFxdHcnIyjz76KPb29kBuK5Hu3btz+vTpEj3j0759+4iKiqJDhw5qR7mtmJgYlixZwvTp0xk4cCA9e/Zk7ty5ACVqJsYbjR07lnXr1vHmm2/SpEkTtFptiZld9t/XFSX9HFfaroPulLcknuPulLc0nOMehBSdSoGgoCBycnI4fPhw/rJz586RkZFRYsbnKAu2b9/O119/zcSJE28qOpQkOTk5XLx4scCyVq1akZ2dTWRkpEqpbm/9+vVcvXqVxo0bExQURFBQEHv27GH69Okl8nU+d+4c58+fL7AsMTERyG2mWxJd+6Dy75OqjY1NiR7nC2Dt2rUl+gM45LbG8vX1LfCBsHr16lhbW9/0t1iSNG7cmJo1a3Lp0iXGjBlTKi6CHR0d8fb25sCBA/nLFEXh+PHjcr4rRFFRUYwfP55OnTrRv39/tePc0cWLFwt86Va7dm08PDxK5JcAmzZtIjMzk/79++ef76ZPn86ePXsICgpi9+7dake8SVxcXIG/N8gdwDY1NbVEnvOufdl6q/OdlZVViRl35lbWrVtHo0aN8gc2LokuXrx405hDBoOBypUrl+jzXeXKlenatSsnT56kV69eJeKLrFtdV5Tkc1xpuQ665k55S+I57nZ5S9M57kFI0akU8PX1pXbt2syaNSt/2bx583BycqJ27doqJis7Tp8+zYsvvkjXrl157LHH1I5zR4cPH6ZTp04FTv5hYWFotVoqVqyoYrJbmzx5MitWrCjwr3bt2gwYMIAVK1aoHe8mc+bMuWmsnhUrVmBtbV1iZ92rU6cOGo2mwLfSmZmZHD58WNXBge8mMjKS06dPl/hul66urpw7d460tLT8ZTt27CA7Ozt/lpGS6rvvviMwMJCePXuqHeWedezYkUWLFuV3Z123bh2xsbGqDmJblqSmpjJy5EicnJx477331I5zR1lZWfTo0YM1a9bkL0tISCAxMRFvb28Vk93awIEDbzrfDRgwgNq1a7NixYoSN0Mc5F4IjRkzpkDXnnXr1mEymUrk+cPe3p6AgICbWmHt3bs3/1xYUm3evLlUnO+AAl90x8fHc+TIkRJ/vlu2bBkpKSkFZgJTy52uK0riOa40XQfBnfOWxHPc7fKWtnPcgyj57eYEAK+++irDhg1j8ODBGI1Gtm7dysSJE0tF08eSLjs7m7Fjx6LX6xkwYABHjhzJX1e5cuX85tslRYMGDahVqxYjR45k3LhxAEyZMoWuXbuWiP7r/3bjrIvX2NnZ4eHhQc2aNVVIdGcDBgzgscce46233qJevXrs2bOHFStWMHz48BI5UxnkTq/ao0cPJk2axPjx43F0dOSnn34iPT29xEzXfSu7du3CaDSWyN+DGzVv3pysrCwGDhxIy5YtSUxMZM2aNQQEBNCiRQu1493WyZMnWbt2Ld9++22JHwj0RiNGjGD16tX069eP+vXrs/b/7d1PSBRvHMfxz/qPyoUyWTskbaiglxXt0F9Q0A0kiCSjNArpkCxiUadMOqQta0WLSquFUlJ56NBunSqsiKzLXuoU0YL0zzyU6SH/JZS/2/5+S21aTc6sv/frNs8zu/Odyzz7fGbm2Tt3VFZWZskJeyLy+XyKRCJqbW3Vq1evou1ZWVmWm1SmpaWppqZGzc3NmpiYkNPpVE9Pj7Kzs1VSUmJ2ed9xOBzfLVbrcDiUnp5u2etceXm52traVFdXp+3bt2t4eFiXLl1SUVGRZa9vBw8elNfr1YoVK1RQUKCHDx/q/v37CgQCZpcW19u3bzU8PKx169aZXcpPrVmzRnl5eTp8+LC2bdumpKQk3bt3T9PT09q9e7fZ5cX15csXXbhwQfv37zf9Buxc8wqrjXGJNg+aq16rjXFz1ZtIY9yfILFIEBs3blRfX58CgYDGxsbk9Xot87hgootEItGL0r59+2L6rl69qg0bNphRVlw2m01dXV1qbW1VU1OT0tLStGPHDkvc2VkMCgsL1dHRIb/fr1AoJKfTqdOnT1t+QUWv16vOzk6dO3dOIyMjysvLU3d3t+k/vn4mHA7L5XJZPjxfvXq1rl27Jr/frxs3bmh6elqFhYU6ceKEpV9Za2tr06ZNCGa/mwAAA/BJREFUmyzzDz7zlZmZqWAwKL/fr0gkotraWjU0NJhd1qJx9+5dffv2TceOHYtpb2ho0KFDh0yqKr6jR48qJSVFXV1dmpqa0ubNm9Xb28ualgax2+3q6elRS0uLTp06pYyMDB04cED19fWWDat37dql5ORk9fb26s2bN8rMzFRLS4vcbrfZpcUVDoeVmppq+TcUbDabLl++rLNnz6q/v1+jo6NyOp3y+/2WeGUtnr6+Ps3MzMjj8ZhdyrzmFVYa4xJtHjRXvVYb4+aq9/8yxtlmF9vS6AAAAAAAADCdNW9hAAAAAAAAIKEROgEAAAAAAMBwhE4AAAAAAAAwHKETAAAAAAAADEfoBAAAAAAAAMMROgEAABgoPz9foVDI7DIAAABMR+gEAAAAAAAAwxE6AQAAAAAAwHCETgAAAAAAADAcoRMAAMBfcOvWLbndbhUXF6uurk6jo6Mx/cFgUBUVFXK5XKqsrNSjR49i+n+0NtT58+dVVlYW3R4aGlJ+fr7C4bBevHghj8ej9evX6/Pnz3/vxAAAAOaJ0AkAAMBg/f39OnPmjKqrq7V37149fvxY7e3t0f4rV66oqalJOTk5amxsVHp6ujwejx48ePBbx3v+/Llqamo0Pj6urVu3KjU11aAzAQAA+H0pZhcAAACw2AwMDCgUCqmgoECS9O7dOz19+lSSND4+rvb2drndbnV2dkqSqqurVVVVJZ/Pp/Ly8l8+XkdHh5qbm1VZWWnYOQAAAPwpnnQCAAAwWEVFRTRwkqTc3FxNTk5Kkp49e6bJyUlVVVVF+5OTk7Vz504NDQ3p9evXv3y80tJSAicAAGA5hE4AAAAGc7lcMdtJSf/+5BobG5MkORyOmH2ysrIkSZ8+fYr7vRMTEz9s/2+ABQAAYBWETgAAAAZbvnx53L6MjAxJ0sePH2PaP3z4IElauXJl3M8ODg7+sH3JkiW/WiIAAMBfR+gEAACwgIqLi7Vs2TIFg8Fo29evXxUKhZSdna21a9dKkpYuXaqRkZHoPoODg3ry5MlClwsAAPDbWEgcAABgAdntdh05ckQ+n0/19fXasmWLbt++rZcvXyoQCMhms0mSioqKdP36dZWUlGhmZkaNjY3KycnR1NSUyWcAAAAwP4ROAAAAC6y2tlZ2u13d3d0aGBhQbm6uLl68qNLS0ug+J0+e1PHjx7Vnzx6tWrVKHo9H79+/182bN02sHAAAYP5ss7Ozs2YXAQAAAAAAgMWFNZ0AAAAAAABgOEInAAAAAAAAGI7QCQAAAAAAAIYjdAIAAAAAAIDhCJ0AAAAAAABgOEInAAAAAAAAGI7QCQAAAAAAAIYjdAIAAAAAAIDh/gHgz2eVzldQ0AAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,9))\n",
    "plt.xticks(range(0,24),pv_detail.index)\n",
    "pv_detail.loc[:,'2'].plot(color= 'steelblue', label ='收藏')\n",
    "pv_detail.loc[:,'3'].plot(color= 'red', label ='加入购物车')\n",
    "pv_detail.loc[:,'4'].plot(color= 'green', label ='支付')\n",
    "plt.legend(loc ='upper center')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:43:42.872524700Z",
     "start_time": "2024-04-15T06:43:42.303849Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# ARPU和ARPPU"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [],
   "source": [
    "data_user_buy = data_user[data_user.behavior_type =='4'].groupby('user_id').size()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:46:03.767457100Z",
     "start_time": "2024-04-15T06:46:01.996466Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id\n100001878    36\n100011562     3\n100012968    15\n100014060    24\n100024529    26\ndtype: int64"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy.head()  #每个用户的购买次数"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:46:09.813515500Z",
     "start_time": "2024-04-15T06:46:09.739556400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "data": {
      "text/plain": "(8886,)"
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy.shape  #有多少用户发生了支付行为"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:46:25.590744500Z",
     "start_time": "2024-04-15T06:46:25.510791500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [
    {
     "data": {
      "text/plain": "count    8886.000000\nmean       13.527459\nstd        19.698786\nmin         1.000000\n25%         4.000000\n50%         8.000000\n75%        17.000000\nmax       809.000000\ndtype: float64"
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy.describe()  #可以知道大部分用户一个月的购买次数是8次"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:46:45.423243500Z",
     "start_time": "2024-04-15T06:46:45.368274600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x =data_user_buy, bins=30)\n",
    "plt.show()\n",
    "#通过图形发现大部分人的购买次数都小于20次，做一下处理，再画直方图"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:47:50.007063400Z",
     "start_time": "2024-04-15T06:47:49.794177600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [
    {
     "data": {
      "text/plain": "(7182,)"
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy[data_user_buy<=20].shape  #有多少用户的购买次数小于20次"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:48:27.241719400Z",
     "start_time": "2024-04-15T06:48:27.158764600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8082376772451046"
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7182/8886"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:48:46.241215100Z",
     "start_time": "2024-04-15T06:48:46.169256500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x648 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#设置图的尺寸\n",
    "plt.figure(figsize=(20,9))\n",
    "plt.hist(x =data_user_buy[data_user_buy<=20], bins=20)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:49:21.100866800Z",
     "start_time": "2024-04-15T06:49:20.790563400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [
    {
     "data": {
      "text/plain": "601.025"
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy.sum()*50/10000  #月ARPU值，这里假设每次花费50元，10000个的用户"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:50:48.245968200Z",
     "start_time": "2024-04-15T06:50:48.157018Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "data": {
      "text/plain": "676.3729462075174"
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 月ARPPU\n",
    "data_user_buy.sum()*50/8886"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:52:37.436902400Z",
     "start_time": "2024-04-15T06:52:37.364939300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 日ARPPU"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [],
   "source": [
    "data_user_buy1= data_user[data_user.behavior_type =='4' ].groupby(['date' , 'user_id'])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:53:06.795351Z",
     "start_time": "2024-04-15T06:53:06.677419300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "outputs": [
    {
     "data": {
      "text/plain": "(49201, 6)"
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.count().shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:53:37.199296300Z",
     "start_time": "2024-04-15T06:53:37.095357500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "outputs": [
    {
     "data": {
      "text/plain": "                      item_id  behavior_type  user_geohash  item_category  \\\ndate       user_id                                                          \n2014-11-18 100001878        1              1             1              1   \n           100014060        2              2             2              2   \n           100024529        6              6             0              6   \n           100027681        3              3             0              3   \n           10004287         2              2             0              2   \n\n                      time  hour  \ndate       user_id                \n2014-11-18 100001878     1     1  \n           100014060     2     2  \n           100024529     6     6  \n           100027681     3     3  \n           10004287      2     2  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>hour</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">2014-11-18</th>\n      <th>100001878</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>100014060</th>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>100024529</th>\n      <td>6</td>\n      <td>6</td>\n      <td>0</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>100027681</th>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>10004287</th>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.count().head()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:53:30.365364900Z",
     "start_time": "2024-04-15T06:53:30.198936200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [
    {
     "data": {
      "text/plain": "        date    user_id   total\n0 2014-11-18  100001878       1\n1 2014-11-18  100014060       2\n2 2014-11-18  100024529       6\n3 2014-11-18  100027681       3\n4 2014-11-18   10004287       2",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>user_id</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014-11-18</td>\n      <td>100001878</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014-11-18</td>\n      <td>100014060</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014-11-18</td>\n      <td>100024529</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014-11-18</td>\n      <td>100027681</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014-11-18</td>\n      <td>10004287</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #按照日期不同用户的购买行为次数\n",
    "data_user_buy1.count()['behavior_type'].reset_index().rename(columns={'behavior_type':' total'}).head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:55:39.939859400Z",
     "start_time": "2024-04-15T06:55:39.852911Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [],
   "source": [
    " #按照日期不同用户的购买行为次数\n",
    "#把上面两步二和一\n",
    "data_user_buy1= data_user[data_user.behavior_type =='4' ].groupby(['date' , 'user_id']).\\\n",
    "count()['behavior_type'].reset_index().rename(columns={'behavior_type':'total'})"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:56:02.878609300Z",
     "start_time": "2024-04-15T06:56:01.796162800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [
    {
     "data": {
      "text/plain": "        date    user_id  total\n0 2014-11-18  100001878      1\n1 2014-11-18  100014060      2\n2 2014-11-18  100024529      6\n3 2014-11-18  100027681      3\n4 2014-11-18   10004287      2",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>user_id</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014-11-18</td>\n      <td>100001878</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014-11-18</td>\n      <td>100014060</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014-11-18</td>\n      <td>100024529</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014-11-18</td>\n      <td>100027681</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014-11-18</td>\n      <td>10004287</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:56:04.544630200Z",
     "start_time": "2024-04-15T06:56:04.494658300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "data_user_buy1.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    1539\n2014-11-19    1511\n2014-11-20    1492\n2014-11-21    1330\n2014-11-22    1411\n2014-11-23    1436\n2014-11-24    1524\n2014-11-25    1497\n2014-11-26    1487\n2014-11-27    1527\n2014-11-28    1442\n2014-11-29    1377\n2014-11-30    1534\n2014-12-01    1657\n2014-12-02    1585\n2014-12-03    1697\n2014-12-04    1585\n2014-12-05    1493\n2014-12-06    1452\n2014-12-07    1403\n2014-12-08    1551\n2014-12-09    1429\n2014-12-10    1442\n2014-12-11    1449\n2014-12-12    3897\n2014-12-13    1549\n2014-12-14    1506\n2014-12-15    1627\n2014-12-16    1650\n2014-12-17    1570\n2014-12-18    1552\nName: total, dtype: int64"
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.groupby('date').count()['total']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:56:56.899023400Z",
     "start_time": "2024-04-15T06:56:56.804074Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18     3730\n2014-11-19     3686\n2014-11-20     3462\n2014-11-21     3021\n2014-11-22     3570\n2014-11-23     3347\n2014-11-24     3426\n2014-11-25     3464\n2014-11-26     3573\n2014-11-27     3670\n2014-11-28     3218\n2014-11-29     3211\n2014-11-30     3616\n2014-12-01     3909\n2014-12-02     3621\n2014-12-03     3885\n2014-12-04     3691\n2014-12-05     3319\n2014-12-06     3272\n2014-12-07     3256\n2014-12-08     3419\n2014-12-09     3449\n2014-12-10     3216\n2014-12-11     3226\n2014-12-12    15251\n2014-12-13     3478\n2014-12-14     3483\n2014-12-15     3764\n2014-12-16     3771\n2014-12-17     3615\n2014-12-18     3586\nName: total, dtype: int64"
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.groupby('date').sum()['total']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-15T06:57:47.816602700Z",
     "start_time": "2024-04-15T06:57:47.719655900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [],
   "source": [
    "#日ARPPU，每一天的不等的\n",
    "data_user_buy2=data_user_buy1.groupby('date').sum()['total']/\\\n",
    "               data_user_buy1.groupby('date').count()['total']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:58:42.687367900Z",
     "start_time": "2024-04-15T06:58:42.615401100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    2.423652\n2014-11-19    2.439444\n2014-11-20    2.320375\n2014-11-21    2.271429\n2014-11-22    2.530120\n2014-11-23    2.330780\n2014-11-24    2.248031\n2014-11-25    2.313961\n2014-11-26    2.402824\n2014-11-27    2.403405\n2014-11-28    2.231623\n2014-11-29    2.331881\n2014-11-30    2.357236\n2014-12-01    2.359083\n2014-12-02    2.284543\n2014-12-03    2.289334\n2014-12-04    2.328707\n2014-12-05    2.223041\n2014-12-06    2.253444\n2014-12-07    2.320741\n2014-12-08    2.204384\n2014-12-09    2.413576\n2014-12-10    2.230236\n2014-12-11    2.226363\n2014-12-12    3.913523\n2014-12-13    2.245320\n2014-12-14    2.312749\n2014-12-15    2.313460\n2014-12-16    2.285455\n2014-12-17    2.302548\n2014-12-18    2.310567\nName: total, dtype: float64"
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:58:45.048497700Z",
     "start_time": "2024-04-15T06:58:44.993529300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    2.423652\n2014-11-19    2.439444\n2014-11-20    2.320375\n2014-11-21    2.271429\n2014-11-22    2.530120\n2014-11-23    2.330780\n2014-11-24    2.248031\n2014-11-25    2.313961\n2014-11-26    2.402824\n2014-11-27    2.403405\n2014-11-28    2.231623\n2014-11-29    2.331881\n2014-11-30    2.357236\n2014-12-01    2.359083\n2014-12-02    2.284543\n2014-12-03    2.289334\n2014-12-04    2.328707\n2014-12-05    2.223041\n2014-12-06    2.253444\n2014-12-07    2.320741\n2014-12-08    2.204384\n2014-12-09    2.413576\n2014-12-10    2.230236\n2014-12-11    2.226363\n2014-12-12    3.913523\n2014-12-13    2.245320\n2014-12-14    2.312749\n2014-12-15    2.313460\n2014-12-16    2.285455\n2014-12-17    2.302548\n2014-12-18    2.310567\nName: total, dtype: float64"
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.groupby('date')['total'].mean()  #另外一种计算方法"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T06:59:43.532741Z",
     "start_time": "2024-04-15T06:59:43.485766700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x648 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#设置画布尺寸\n",
    "plt.figure(figsize=(20,9))\n",
    "data_user_buy2.plot()  #因为数据是11月18号到12月18号的\n",
    "#设置xticks\n",
    "plt.xticks(data_user_buy2.index[::3],data_user_buy2.index[::3],rotation=45)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-15T07:04:02.296537900Z",
     "start_time": "2024-04-15T07:04:01.308100300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "data_user_buy2.describe()   #发现每天大家的平均购物次数是2.3次,双12时达到峰值，3.9次\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#如果计算日ARPU，直接除以10000"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
